Automated Contouring of Cervical Lymph Nodes and Clinical Target Volumes for Nasopharyngeal Carcinoma Based on Deep Learning and Experience Constraints

PURPOSE/OBJECTIVE(S)

Application of artificial intelligence (AI) for automated contouring of tumor volumes and organs at risk (OARs) for radiotherapy of nasopharyngeal carcinoma (NPC) leads to improved contouring accuracy and efficiency. However, few studies have involved the automated contouring of gross tumor volume of cervical lymph nodes (GTVn) and clinical target volumes (CTVs). In this work, we proposed an AI automated contouring tool for GTVn and CTVs for radiotherapy of NPC on the plain scans of planning compute tomography (CT).

MATERIALS/METHODS

In this retrospective study, plain scan datasets of planning CT covering the nasopharynx and neck from 139 patients with NPC between March 2022 and December 2022 were collected and divided into training, validation, and testing cohorts of 95, 24, and 20 patients, respectively. Ground truth contours of primary gross tumor volume (GTVp), GTVn (divided into GTVn_L in left neck and GTVn_R in right neck), CTVs (including high risk CTV1 contains GTVp and low risk CTV2 contains GTVp and cervical nodal levels) and OARs were delineated and were defined by consensus of two experts. We first proposed a three-dimensional (3D) U-net using GTVp and OARs as experience constrains to guide the automated delineation of GTVn and CTVs. The average Dice similarity coefficient (DSC) and average surface distance (ASD) were used to quantify the performance of the AI tool. Next, five prospective patients were enrolled for clinical evaluation of our AI tool. DSC between automated contours and radiation oncologist-revised contours and time consuming of the revision were record.

RESULTS

Clinical characteristics of 139 retrospective and 5 prospective patients are list in Table 1. In the independent testing set of 20 patients, our AI tool showed high performance in GTVn and CTVs contouring when compared with the ground truth contours. The mean DSC were 0.73 ± 0.07, 0.74 ± 0.05, 0.93 ± 0.03, and 0.88 ± 0.03, and the mean ASD were 1.01 ± 0.43 mm, 1.14 ± 0.61 mm, 0.51 ± 0.13 mm, 1.17 ± 0.43 mm for GTVn_L, GTVn_R, CTV1 and CTV2, respectively. In the five prospective patients, mean DSC were 0.74 ± 0.07, 0.74 ± 0.10, 0.95 ± 0.01 and 0.89 ± 0.04, respectively. The median time consuming for GTVn and CTVs revision was 2minutes and 10 seconds (range, 1 minutes to 3 minutes).

CONCLUSION

The proposed AI tool integrating clinical experience as constrains showed high accuracy for contouring GTVn and CTVs of NPC. With the assistance of AI contours, contouring efficiency could be probably increased, which is promising in online adaptive radiotherapy of NPC.

First Implementation of Full-Workflow Automation for Online Adaptive Radiotherapy of Nasopharyngeal Carcinoma

PURPOSE/OBJECTIVE(S)

The aim of this work is to established the technical characteristics and implementation procedures of an artificial intelligence (AI)-powered radiotherapy workflow that enables full-process automation for online adaptive radiotherapy (ART); and evaluate its feasibility and performance implemented for ART of nasopharyngeal carcinoma (NPC).

MATERIALS/METHODS

This single center, prospective study has been approved by the ethical committee of the institution. The online ART workflow was developed based on a CT-integrated linear accelerator. During the course of radiotherapy, the patient underwent daily pre-treatment fan-beam CT (FBCT) scan. Then the FBCT was automatically registered to the original planning CT and used to assess the need for the patient to implement ART according to radiation oncologist's discretionary. The online ART workflow incorporates critical radiotherapy procedures from re-simulation, auto-segmentation by integrating image fusion and deep learning method, auto-replanning, beam delivery, and in vivo quality assurance (QA) into one scheme, while the patient is on the treatment couch during the whole process.

RESULTS

From 2th April 2022 to 5th January 2023, 20 patients with newly-diagnosed, non-metastatic NPC were enrolled in this study. Only one-time online ART was performed for each patient, because that the appropriate timing for triggering online ART was explored in parallel with this study. According to radiation oncologists' discretionary, the median fraction for performing online ART was at 21 fractions (interquartile range, 19-24 fractions). All patients were well tolerated and successfully completed the treatment. For tumor targets contouring, minor revisions were required for automated contours of the primary gross tumor volume (GTVp) and clinical target volumes (CTVs, including CTV1 and CTV2), with the mean DSC between before and after revision of 0.91±0.042, 0.94 ± 0.042 and 0.91 ± 0.061, respectively; and much more revisions for the automated contours of cervical lymph nodes GTV (GTVn), with the mean DSC of 0.74 ± 0.28. The automated contours of normal tissues were clinically acceptable with little modifications. Median time consuming for auto-segmentation and revision was 9.5 minutes (min). For treatment planning, 18 automated plans (90%) were passed at their first auto-optimization and two plans (10%) were passed after further optimization of the dose coverage of CTVs by physicist; and the median time consuming for auto-planning was 6.2 min. Time consuming for other procedures were as follows: re-simulation, 2.3 min; plan evaluation, 3.3 min; beam delivery, 4.6 min; and the duration of the entire process was 25.9 min, range from 19.4 min to 32.5 min.

CONCLUSION

We successfully established an AI-powered online ART workflow for adaptive radiotherapy of NPC, and confirmed that current auto-segmentation and auto-replanning methods are powered enough to support the clinical application of its online ART.

Preserving Structural Consistency in the Generation of Synthetic CT in Pelvic MR-Only Radiation Treatment Planning

PURPOSE/OBJECTIVE(S)

MR-based synthetic CT (sCT) generation is necessary for MR-only radiotherapy to assist in radiation dose calculation, owing to no electronic density information in MR images. This study investigated the feasibility of synthesizing CT images from magnetic resonance (MR) images using generation antagonism networks (GANs) for MR radiotherapy of rectal cancer. Meanwhile, the transformer module and the contrast learning loss were introduced to improve the sCT.

MATERIALS/METHODS

The data set used in this study was the T2-weighted MR and CT image data of 108 patients with rectal cancer. Three-fold cross-validation was performed on all data sets. The transformer module was introduced into the plain CycleGAN, and the improved Patch Noise Contrastive Estimation (PatchNCE) loss was used as the loss function. The improved PatchNCE loss maintained the structural consistency of the MR and the synthetic CT by ensuring the consistency of the distribution of image patches on the MR-sCT image pair. The 2.5D images were taken as the input of our model, which refers to taking two consecutive adjacent layers in a specific layer. The CT-to-sCT image similarity was evaluated by metrics of mean absolute error (MAE), peak signal-to-noise ratio (PSNR), and Structure Similarity Index Measure (SSIM). The sCT dosimetric accuracy was verified against CT-based dose distributions for the photon plan. Relative dose differences in the planning target volume and organs at risk were computed.

RESULTS

The evaluation indicators of sCT images generated by our model were superior to the plain CycleGAN in the results of the three-fold cross-validation. MAE, PSNR and SSIM of our model were 42.850HU, 26.486 and 0.988, respectively, which were superior to 47.129HU, 25.167 and 0.978 of the plain CycleGAN. In addition, sCT generated by our model exhibited good continuity in the axial direction compared with plain CycleGAN. Furthermore, most of the relative differences in the DVH indicators were less than 1%.

CONCLUSION

The accuracy of sCT can be effectively improved by introducing a transformer module and comparative learning loss function. Moreover, all dosimetric differences were within clinically acceptable criteria for photon radiotherapy, demonstrating the feasibility of the MRI-only workflow for patients with rectal cancer.

Cost-Effectiveness of Short-Course Radiotherapy Based Total Neoadjuvant Therapy for Locally Advanced Rectal Cancer in China

PURPOSE/OBJECTIVE(S)

The phase III STELLAR (NCT02533271) trial demonstrated that four cycles of chemotherapy after short-course radiotherapy (SCRT-TNT) were not inferior to the standard care of long-course concurrent radiotherapy (LCRT) in patients with locally advanced rectal cancer (LARC). This study assessed the cost-effectiveness of SCRT-TNT versus LCRT in locally advanced rectal cancer in China on the basis of the STELLAR trial.

MATERIALS/METHODS

A Markov model was used to synthesize the healthcare costs and benefits of LARC patients based on results from the STELLAR trial. The model assumes that LARC who meet the inclusion criteria of the STELLAR trial experience four possible states: No Evidence of Disease (NED), locally recurrence, distant metastases, or any death from rectal cancer or other unrelated causes, where local recurrence continues to be classified as resectable and unresectable. The transition status period is 3 month, and 5 years is used to calculate direct medical costs and health benefits. The probabilities of states transition after SCRT-TNT or LCRT were derived from the results of the STELLAR trial and previous published article (Table.1). Costs were evaluated from the Chinese payer's perspective reported in early 2022 US dollars (US$1 = 6.78 Chinese Yuan). Sensitivity analyses were performed for key variables. Cost-effectiveness was evaluated using the incremental cost-effectiveness ratio and net monetary benefits. Effectiveness was defined as quality-adjusted life-years (QALYs). Willingness-to-pay (WTP) threshold was set at $43500/QALY. Data were collected from October 3, 2020, to September 20, 2021, and analyzed from November 15, 2020, to October 25, 2021.

RESULTS

During the 5-year horizon, for the base case scenario, SCRT-TNT incurred a lower total cost and higher QALYs compared with LCCRT. The total cost was $65767 and QALYs were 1.77 for SCRT-TNT; for LCCRT, the total cost was $72802 and QALYs were 1.64. This resulted in an ICER of -$ 55470.69 per QALY. Therefore, SCRT-TNT was a cost-saving and dominating treatment strategy compared with LCRT. Sensitivity analysis showed that ICERs were most sensitive to the parameters of distant metastases risk after treatment.

CONCLUSION

SCRT-TNT in locally advanced rectal cancer can be a cost-effective alternative to LCRT in China, and should be considered in appropriately selected patients.

A Multicenter Study on Deep Learning for Glioblastoma Auto-Segmentation with Prior Knowledge in Multimodal Imaging

PURPOSE/OBJECTIVE(S)

A precise radiotherapy plan is required to ensure accurate delineation of gross tumor volumes (GTV) and clinical target volumes (CTV1 and CTV2) of glioblastomas (GBMs). However, traditional manual delineation is labor intensive and highly dependent on oncologists' experience. To construct and evaluate a deep learning-based automatic delineation method using prior knowledge in multimodal medical imaging to automate precise GTV, CTV1 and CTV2 contouring in GBM patients.

MATERIALS/METHODS

We retrospectively collected the CT and MRI scans of 55 eligible patients with histologically proven high-grade glioma (HGG) from an institute, these scans were performed with non-enhanced CT (CT), contrast-enhanced T1-weighted (T1C) and T2-FLAIR (T2F) sequences. We proposed a two-stage automatic segmentation framework (PKMI-Net) for GTV, CTV1 and CTV2 based on deep learning using prior knowledge in multimodal medical imaging, and its segmentation performance was evaluated with dice similarity coefficient (DSC), 95% Harsdorff distance (HD95), average surface distance (ASD) and relative volume difference (RVD). To further investigate the generalizability of our method, we designed and conducted two evaluation strategies (Mix and Cross) on four multicenter datasets (including 55 patients, 37 patients, 21 patients and 35 patients).

RESULTS

The evaluation results with an 11-patient test set from the single institute were summarized in Table 1, the proposed method demonstrated the best accuracy in segmenting, respectively, GTV, CTV1 and CTV, achieving a DSC of 0.94, 0.95 and 0.92; HD95 of 2.07 mm, 1.18 mm and 3.80 mm; ASD of 0.69 mm, 0.39 mm and 1.13 mm and RVE of 5.50%, 3.97% and 9.68%. In the multicenter evaluation, the segmentation performance of our method implemented with the Cross strategy was comparable to that with the Mix strategy, demonstrating that our method had high and stable generalizability across multicenter datasets in automatically segmenting GTV, CTV1 and CTV2.

CONCLUSION

Our proposed method achieved promising results in automatically segmenting gliomas across various datasets, which could improve the quality and efficiency of glioblastoma radiotherapy.

Virtual-Collimator Based Spatial Dose Modulation for Proton GRID Therapy

PURPOSE/OBJECTIVE(S)

Compared to conventional proton therapy, the proton GRID therapy can substantially improve normal tissue protection (with the delivery of spatially-modulated peak-valley dose pattern to normal tissues) while maintaining the tumor control efficacy (with the delivery of uniform dose pattern to tumor targets). The realization of proton GRID often relies on the use of physical collimators to shape the spatial dose distribution. However, the physical collimator may increase neutron dose, decrease delivery efficiency, and limit the freedom for patient positioning. Here we propose a virtual-collimator (VC) method for proton GRID. This new approach can generate peak-to-valley pattern with high peak-to-valley dose ratio (PVDR), without using a physical collimator.

MATERIALS/METHODS

The principle behind the VC method to modulate the spatial dose distribution consists of two major steps: (1) the primary beam is essentially halved, i.e., the beamlets are interleaved, so that the organ-at-risk (OAR) plane has the peak-valley dose pattern, while the target plane also has the valley dose; (2) the complementary beam is added with half complementary beamlets to fill in the previously valley-dose positions at the target plane, so that the target dose is uniform, while on the other hand, the complementary beam is angled slightly from the primary beam, so that the OAR still has the peak-valley dose pattern. Moreover, on top of VC, we also utilize sparsity regularization method using total variation and L1 sparsity (TVL1) to further jointly optimize PVDR and dose objectives, namely VC-TVL1.

RESULTS

VC and VC-TVL1 were validated in comparison with conventional proton GRID treatment planning method via IMPT ("CONV") and TVL1-based proton GRID treatment planning method without VC ("TVL1"), for a prostate case with single-beam (270° only) or two-beam (90° and 270°) scenarios. As shown in the table, the results show that VC can indeed modulate spatial dose with higher PVDR than CONV or even TVL1. VC had higher spatial modulation frequency with smaller peak-to-peak distance than TVL1. Moreover, VC+TVL1, as the synergy of VC and TVL1, further improved PVDR from VC or TVL1 alone.

CONCLUSION

A new way to deliver proton GRID therapy without a physical collimator is developed using the VC method. The VC method can be synergized with TVL1 optimization algorithm to further jointly optimize PVDR and dose objectives.

Prediction of Response to Neoadjuvant Chemoradiotherapy Combined with Pembrolizumab in Esophageal Squamous Cell Carcinoma with CT/FDG PET Radiomic Signatures Based on Machine Learning Classification

PURPOSE/OBJECTIVE(S)

PALACE-1 trial has confirm that the addition of pembrolizumab to neoadjuvant chemoradiotherapy (NCRT) improves the pathological complete response(pCR) for esophageal squamous cell carcinoma (ESCC), which might be a novel treatment strategy for ESCC. In the present study, we aim to establish a machine learning model to predict the local response to NCRT+ pembrolizumab for ESCC by using pretreatment 18-fluorodeoxyglucose positron emission tomography (FDG PET) and contrast-enhanced plan CT images.

MATERIALS/METHODS

A total of 65 cases treated with NCRT+ pembrolizumab followed by surgery were prospectively enrolled for analysis from 2019-2022. Each patient contains a contrast-enhanced plan CT and FDG PET images. 52 patients were randomly divided into training set and 13 patients were used as test set. The Extraction of radiomics features was performed using an open-source Python library PyRadiomics automatically. Features were computed according to the radiologist-drawn ROIs on both CT and PET images. In the feature selection stage least absolute shrinkage and selection operator (LASSO) was utilized on CT features and PET features separately. Four different machine learning models were implemented: Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF) and XGBoost (XGB). The features selected by LASSO regression were used as model input and the output of the model is "pCR" or "non-pCR". To find the optimal parameter, the 5-fold cross-validation method was used in the training stage. In this study, we use accuracy, sensitivity and specificity as the metrics to evaluate the performance of the model on the testing cohort. The predictive performance of the model was assessed using the area under curve (AUC) of the receiver operating characteristics curve (ROC).

RESULTS

Of the 65 cases treated with NCRT+pembrolizumab, 35 patients archived pCR (53.8%), and 30 archived non-pCR. 1684 radiomics features were extracted from each case, and half of them (842 features) were from CT and others were from PET. Among the machine learning models mentioned above SVM achieves the most promising performance on the evaluation metrics. Accuracy, sensitivity, specificity and AUC score on test set were 0.692, 0.833, 0.571 and 0.786 for CT features and 0.615, 0.667, 0.571 and 0.762 for PET features, respectively. For CT+FDG PET fused features accuracy, sensitivity, specificity and AUC score on test set were 0.769, 0.667, 0.857 and 0.833.

CONCLUSION

In this study, we performed several different machine learning models to predict the response to NCRT+ pembrolizumab among ESCC based on the extracted radiomics features from CT and FDG PET images. The best-performing model based on radiomics features of CT and PET images could identify non-pCR to NCRT + pembrolizumab in EC patients.

Dosimetric Benefits of Online Adaptive Radiotherapy in Nasopharyngeal Carcinoma

PURPOSE/OBJECTIVE(S)

Online adaptive radiotherapy (ART) has the advantage of compensating for potential underdosing to targets and overdosing to organs-at-risk (OARs) caused by variations in patient anatomy and tumor geometry. Artificial intelligence (AI)-assisted rapid generation of new plans makes online ART possible. We aimed to evaluate the dosimetric benefits of online ART on tumor coverage and OARs sparing in nasopharyngeal carcinoma (NPC).

MATERIALS/METHODS

Twenty patients diagnosed with NPC (19 with stage III and 1 with stage II according to the 8th edition of the AJCC/UICC staging system) who underwent definitive radiotherapy or concurrent chemoradiotherapy and received online ART on CT-Linac between April 2022 and December 2022 were included in this study, consisting of 14 males and 6 females with a median age of 48 years (range: 29-68 years). The prescription dose was 6996 cGy/33 fractions for primary gross tumor volume (GTVp), 6600-6996 cGy/33 fractions for gross tumor volume of nodes (GTVn), 6006 cGy/33 fractions for high-risk clinical tumor volume (CTV1), 5412 cGy/33 fractions for low-risk clinical tumor volume (CTV2). The majority of the patients (15/20) received online ART during the fourth to fifth week of their radiotherapy treatment The auto-segmented contours and auto-plan generated by AI were manually reviewed and edited by radiotherapists and physicists. The paired samples t-test was used to compare the dose and volumes metrics of targets and OARs between scheduled plan and online ART plan.

RESULTS

The results of this study showed that compared to the scheduled plan, the online ART plan resulted in significant reductions in the volumes of all targets and 8/12 OARs (temporal lobes, optic nerves, lenses, eyes, parotids, submandibulars, mandibles, and thyroid) (P<0.05). The online ART plan also improved target coverage, with D98% for GTVp in the scheduled plan compared to the online ART plan being 7063.4 ± 76.1 cGy and 7096.1 ± 53.9 cGy (P = 0.1), CTV1 being 6266.7 ± 114.9 cGy and 6208.7 ± 54.7 cGy (P<0.05), and CTV2 being 4142.5 ± 1700.9 cGy and 5416.4 ± 23.8 cGy (P<0.01), respectively. The dose to all 12 OARs was reduced with the use of online ART, with 5/12 OARs showing statistical significance. The D0.03cm3 for the spinal cord in the scheduled plan and online ART plan were 3630.9 ± 197.6 and 3454.1 ± 132.0 cGy; for the temporal lobes were 7075.2 ± 303.0 and 6994.2 ± 345.1 cGy; and 4396.0 ± 2575.0 and for the pituitary were 4214.5 ± 2499.2 cGy. Meanwhile the Dmean for the eyes in the scheduled plan and online ART plan was 769.0 ± 232.0 and 714.8 ± 200.1 cGy; and for the mandibles were 3187.7 ± 211.5 and 3066.0 ± 152.1 cGy.

CONCLUSION

Online ART was effective in protecting most of the OARs in NPC patients, while simultaneously indicating a trend towards enhancing target coverage. This study demonstrated the promising potential of online ART for patients with NPC. This approach will be tested in an upcoming phase III trial.

2V-CBCT: Two-Orthogonal-Projection Based CBCT Reconstruction and Dose Calculation from Real CBCT Projection Data

PURPOSE/OBJECTIVE(S)

Not all radiation therapy (RT) treatments/fractions have CBCT acquired, but two orthogonal projections (i.e., KV radiography) are always available. This work demonstrates the feasibility of two-orthogonal-projection-based CBCT (2V-CBCT) reconstruction and dose calculation for RT from real CBCT projection data, which is the first 2V-CBCT feasibility study using real projection data, to the best of our knowledge.

MATERIALS/METHODS

2V-CBCT is a severely ill-posed inverse problem for which we propose a coarse-to-fine learning strategy. First, a 3D deep neural network that can extract and exploit the inter-slice and intra-slice information is adopted to predict the initial 3D volumes. Then, a 2D deep neural network is utilized to fine-tune the initial 3D volumes slice-by-slice. During the fine-tuning stage, a perceptual loss based on multi-frequency features is employed to enhance the image reconstruction. Dose calculation results from both photon and proton RT demonstrate that 2V-CBCT provides comparable accuracy with full-view CBCT based on real projection data.

RESULTS

The proposed method was evaluated on real HN data acquired from on-board CBCT scanners rather than the low-resolution simulated data or down-sampled data. Both visual assessment and quantitative analysis demonstrate that the proposed coarse-to-fine learning strategy has the potential to produce satisfactory volumetric images from two orthogonal projections. Furthermore, we assessed the utility of 2V-CBCT in RT. The results show that the dose distribution maps, dose-volume histograms, and dose parameters calculated using 2V-CBCT have comparable accuracy with the counterparts calculated using the corresponding full-view CBCT for both photon and proton RT. In the table, the methods under comparison are pCT (planning CT), FV-CBCT (CBCT reconstructed with full-view projection data), and 2V-CBCT (CBCT reconstructed with two orthogonal projections).

CONCLUSION

A new effective 2V-CBCT reconstruction method is proposed and validated using real CBCT projection data, which can potentially provide comparable dose calculation accuracy for both photon and proton RT.

Hsa_circ_0003928 regulates the progression of diabetic nephropathy through miR-136-5p/PAQR3 axis

BACKGROUND

Diabetic nephropathy (DN) is one of the complications of diabetes and has a high mortality, but its specific pathogenesis is not clear. In recent years, researches on the mechanism of circRNAs in DN have been proved a lot, whereas the functional mechanism of circ_0003928 in DN remains open and it must be investigated to value its important role in DN prevention.

METHODS

HK-2 cells were treated with high glucose (HG), normal glucose (NG) or Mannitol. Cell counting kit-8 (CCK8) and 5-ethynyl-2'-deoxyuridine (EdU) assays were performed to detect cell proliferation. Enzyme-linked immunosorbent assay (ELISA) was applied to analyze malondialdehyde (MDA) and superoxide dismutase 1 (SOD) levels. Flow cytometry and western blot were preformed to measure cell apoptosis. Real-time quantitative PCR (RT-qPCR) was used to test the levels of circ_0003928, miR-136-5p and progestin and adipoQ receptor family member 3 (PAQR3) mRNA. Western blot was executed to detect Bcl2 associated X (Bax), B cell leukemia/lymphoma 2 (Bcl2), smooth muscle (αSMA), apolipoprotein (C-IV) and PAQR3 levels. Luciferase reporter assay and RNA pull-down assay were used to analyze the target relationship between miR-136-5p and circ_0003928 or PAQR3.

RESULTS

Circ_0003928 and PAQR3 expression were up-regulated, whereas miR-136-5p was decreased in DN serum and HG-induced HK-2 cells. Circ_0003928 knockdown promoted cell proliferation, and inhibit cell apoptosis, oxidative stress, and fibrosis in HK-2 cells under HG condition. MiR-136-5p silencing overturned the protective effects of si-circ_0003928 on HG-induced HK-2 cells. MiR-136-5p was targeted by circ_0003928 and directly targeted PAQR3. Overexpression of PAQR3 counteracted the inhibitory functions of circ_0003928 knockdown or miR-136-5p overexpression on HG-induced HK-2 cell injury.

CONCLUSION

Circ_0003928 acted as a sponge of miR-136-5p to up-regulating PAQR3 expression, and then regulate the proliferation, oxidative stress, fibrosis and apoptosis in HG-induced HK-2 cells.

Small Samples and Low-Cost Auto-Segmentation Method for Pelvic Organ-at-Risk Segmentation in Magnetic Resonance Images Using Deep-Learning

PURPOSE/OBJECTIVE(S)

In radiotherapy, magnetic resonance (MR) imaging has higher contrast of soft tissue, and no radiation compared with computed tomography (CT) scanning. Due to the high-cost of manual annotation, the deep-learning based automatic organ-at-risk (OAR) and target delineation algorithms are in high-demand, but the collecting of large amounts of high-quality annotated datasets remains difficulty. In this paper, we proposed a low-cost OAR segmentation method with semi-supervised annotation using small annotation samples of pelvic MR images.

MATERIALS/METHODS

This study consisted of 94 patients diagnosed with rectal cancer from April 2018 to March 2021 at Peking University People's Hospital. We used 17 slices of MR images with annotation and 78 slices without annotation to train a deep-learning based segmentation model. The bladder, femoral heads, rectum and small intestine were selected as OAR. Semi-supervised method and ensemble learning were used for generating training set using small sample with annotation. Post-processing algorithm was used to correct the self-annotation data. Two of 14 annotation samples were set as test set. As for un-labeled images, 40 of them were set as semi-supervised annotation train set, the rest were test set. Besides, both 2D and 3D auto-segmentation networks were evaluated.

RESULTS

The dice of bladder, femoral head left and right, rectum and small intestine between segmentation results and reference masks is 0.947, 0.983, 0.981, 0.900, 0.845 only using self-annotation and post-processing method of 2D segmentation model. And the dice of corresponding OAR is 0.871, 0.975, 0.975, 0.783, 0.724 using 3D segmentation network, 0.885,0.982, 0.982, 0.882, 0,814 using 2D segmentation network with supervised method (nnUNet). The 2D model outperformed 3D model with better segmentation performance, shorter inference time and fewer parameters.

CONCLUSION

The results proved that we can train a multi-OAR segmentation model only using small annotation samples and other unlabeled samples. Ensemble learning and post-processing methods are necessary for semi-supervised data annotation. For anisotropy data, 2D model shows better performance than 3D models.

Unsupervised Domain Adaptation of Auto-Segmentation on Multi-Source MRIs

PURPOSE/OBJECTIVE(S)

Deep learning has achieved great success in medical image segmentation. Most existing deep learning (DL) approaches make no adjustments to the model prior to inference. These models can perform well on the data of the same distribution, but their performance usually degrades when applied to the images from different source, i.e., different scanners. To tackle the problem caused by domain shift, we proposed an unsupervised domain adaptation (UDA) method based on entropy minimization and physical consistency constraints.

MATERIALS/METHODS

The proposed method combines feature-level and instance-level domain adaptation techniques to transfer knowledge from the source to the target domain. Specifically, the feature-level adaptation technique uses a graph-based entropy minimization to reduce the discrepancy between the source and target domains. The instance-level adaptation technique employs a novel consistency loss to regularize the physical consistency of the same object, such as volume, length, and centroid, thus improving the segmentation accuracy of the target domain. A collection of 93 abdominal MR images, comprising 45 cases from a 0.35T MRI scanner (TRUFI) and 48 cases from a 1.5T MRI scanner (T2), was utilized to evaluate the effectiveness of the proposed method. The contours of 6 organs-at-risk were delineated by a senior radiation oncologist, serving as the ground truth. Three models, the source model (SRC) trained on the source domain, the target model (TGT) trained on the target domain, and the UDA model adapted from the source domain to the target domain, were compared on the target domain using the Dice Similarity Coefficient (DSC).

RESULTS

In the experiment of 0.35T-to-1.5T, the proposed UDA method outperformed the source model, achieving an average DSC score of 0.82 ± 0.11, compared to 0.58 ± 0.23 (SRC) and 0.85 ± 0.09 (TGT), respectively. In the inverse experiment 1.5T-to-0.35T, the UDA model achieved an average DSC score of 0.79±0.13, compared to DSCs of 0.52 ± 0.25 and 0.81 ± 0.11 for the SRC and TGT respectively. The UDA method yielded a significant improvement of 46%, compared to the SRC. Particularly, OARs (organ at risk) with higher deformability such as the stomach and duodenum achieved a 58% and 63% improvement in performance, respectively.

CONCLUSION

This work presents a compelling approach of UDA for auto-segmentation on multi-source MRIs. Experimental results demonstrate that the UDA effectively improve the segmentation performance of the source model in the target domain, resulting in a more robust segmentation model.

Vertex Position Optimization for LATTICE Therapy

PURPOSE/OBJECTIVE(S)

LATTICE radiation therapy (RT) aims to deliver 3D heterogenous dose of high peak-to-valley dose ratio (PVDR) to the tumor target, with peak dose at lattice vertices inside the target and valley dose for the rest of the target. In current clinical practice the lattice vertex positions are constant during treatment planning. This work proposes a new LATTICE plan optimization method that can optimize lattice vertex positions as plan variables, which is the first lattice vertex position optimization study to the best of our knowledge.

MATERIALS/METHODS

The new LATTICE treatment planning method optimizes lattice vertex positions as well as other plan variables (e.g., photon fluences or proton spot weights), with optimization objectives for target PVDR and organs-at-risk (OAR) sparing. To satisfy mathematical differentiability, the lattice vertices are approximated in sigmoid functions. For geometric feasibility, proper geometry constraints are enforced onto the lattice vertex positions. The lattice vertex position optimization problem is solved by iterative convex relaxation method, where lattice vertex positions and photon/proton plan variables are jointly updated via the Quasi-Newton method.

RESULTS

Both photon and proton LATTICE RT were considered, and the optimal lattice vertex positions in terms of plan objectives were found by solving all possible combinations on given discrete positions via heuristic searching based on standard IMRT/IMPT, which served as the ground truth for validating the new LATTICE method ("NEW"). That is, the plan with the smallest optimization objective ("BEST"), the plan with the median optimization objective ("MID"), and the plan with the largest optimization objective ("WORST") were selected as the reference plans to be compared with NEW. The table was for an abdomen case with the large bowel as the OAR, where the parameters are total optimization objective f, the mean valley dose of target Dvalley, the mean peak dose of target Dpeak, PVDR = Dpeak/Dvalley, and the mean dose of large bowel Dbowel. The unit of doses is Gy. The results in the table show that the new method indeed provided the optimal lattice vertex positions with the smallest optimization objective, the largest target PVDR, and the best OAR sparing.

CONCLUSION

A new LATTICE treatment planning method is proposed and validated that can optimize lattice vertex positions as well as other photon or proton plan variables for improving target PVDR and OAR sparing.

The Pathogenesis of COVID-19-Related Taste Disorder and Treatments

COVID-19, mainly manifested as acute respiratory distress syndrome, has afflicted millions of people worldwide since 2019. Taste dysfunction is a common early-stage symptom of COVID-19 infection that burdens patients for weeks or even permanently in some cases. Owing to its subjectivity and complexity, the mechanism of taste disorder is poorly studied. Previous studies have reported that the COVID-19 entry receptors are highly expressed in taste buds, thereby intensifying the cytocidal effect. Taste receptor cells are vulnerable to inflammation, and the COVID-19-induced cytokine storm causes secondary damage to taste function. Interferon and various proinflammatory cytokines can trigger cell apoptosis and disrupt the renewal of taste bud stem cells. This immune response can be further enhanced by the accumulation of Angiotensin II (Ang II) caused by an unbalanced local renin-angiotensin system (RAS) system. In addition, severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) is neurotropic and can invade the brain through the olfactory bulb, affecting the nervous system. Other factors, such as host zinc deficiency, genetic susceptibility, sialic acid, and some neurotransmitters, also contribute to the pathogenesis process. Although several medical interventions have displayed effectiveness, only a few strategies exist for the treatment of postinfectious dysgeusia. Stem cell-based taste regeneration offers promise for long-term taste disorders. Clinical studies have demonstrated that stem cells can treat long COVID-19 through immune regulation. In dysgeusia, the differentiation of taste bud stem cells can be stimulated through exogenous epithelial-derived and neural-derived factors to regenerate taste buds. Tongue organoids are also emerging as functional taste buds, offering new insights into the study of taste regeneration. This review presents the current evidence of the pathogenesis of COVID-19-related dysgeusia, summarizes currently available treatments, and suggests future directions of taste regeneration therapy.

[Recommendations for the diagnosis and treatment of IgG4-related disease in China]

IgG4-related disease (IgG4-RD) is an immune-mediated fibroinflammatory condition characterized by tumefactive lesions in multi-organs. It is a novel entity presented by variable manifestations. In recent years, there has been progress toward recognizing IgG4-RD. However, the diagnosis and treatment of IgG4-RD still present challenges due to insufficient experience. To address this, the Chinese Rheumatology Association has developed standardized guidelines for the diagnosis and treatment of IgG4-RD based on domestic and international experience. These guidelines aim to enhance the understanding and management of IgG4-RD, ultimately improving the prognosis for patients with IgG4-RD.

Deep Learning-Based Synthesis of Contrast-Enhanced MRI for Automated Delineation of Primary Gross Tumor Volume in Radiotherapy of Nasopharyngeal Carcinoma

PURPOSE/OBJECTIVE(S)

Contrast-enhanced MRIs are necessary to delineate the primary gross tumor volume (GTVp) in radiotherapy of nasopharyngeal carcinoma (NPC). However, using contrast agents to scan contrast-enhanced MRIs is not applicable to some patients due to metal implants or their allergy, and it increases the treatment cost of patients. To address these problems, this work aims at synthesizing contrast-enhance MRIs from unenhanced MRIs by implementing generative adversarial network (GAN).

MATERIALS/METHODS

In this work, 324 MRI datasets of patients with NPC were retrospectively collected between September 2016 and September 2017 from a single institute. MRI examinations were performed with un-enhanced T1-weighted (T1) and T2-weighted (T2) sequences, and contrast-enhanced T1-weighted (T1C) and fat-suppressed T1-weighted (T1FSC) sequences. We designed and developed a modified pix2pix network to synthesize T1C (sT1C) and T1FSC (sT1FSC) from real T1. The end of the generator in this network was assembled with multiple heads (the classification head and gradient head) to learn more representation information and features from real images, the discriminator in this network distinguished whether the synthesized image is real and fake and supervised that the generator outputs more realistic synthesized image. We verified the performance of the synthesized images for automated delineation of GTVp. In an independent testing set of 11 patients, the synthesized sT1C and sT1FSC were inputted into the segmentation deep learning network along with their corresponding T1 and T2 sequences to generate GTVp contours. Delineation performance of the synthesized images and real images for automated delineation were evaluated by dice similarity coefficient (DSC), and average surface distance (ASD), using human expert contours as the ground truth.

RESULTS

In automated contouring of GTVp for NPC, the segmentation deep learning network using one or two synthesized MRIs showed equivalent performance when compared with the automated contours which generated from four real MRI sequences. Mean DSCs between automated contours by sT1C-replaced or sT1C and sT1FSC-replaced network and ground truth contours were 0.726 ± 0.143 and 0.711 ± 0.157, respectively, slightly inferior to that of contours generated from four real MRI sequences (0.740 ± 0.154, both P >0.05). In terms of mean ASD, there was also no significant difference between automated contours generated from synthesized images and real images (3.056 ± 4.216 mm and 3.537 ± 4.793 mm vs. 3.124 ± 4.637 mm; both P > 0.05).

CONCLUSION

We proposed an MRI-synthesis method based on GAN and the synthesized contrast-enhanced MRIs performed equivalent as the real contrast-enhanced MRIs in the automated delineation of gross tumor volume for radiotherapy of NPC.

Clinical effectiveness of semaglutide on weight loss, body composition, and muscle strength in Chinese adults

OBJECTIVE

The aim of this study was to investigate the clinical effectiveness of semaglutide on weight loss, body composition and muscle strength in the Chinese population with obesity.

PATIENTS AND METHODS

Data were retrospectively analyzed for participants prescribed semaglutide in 2021 and 2022 from a Chinese weight management clinic. Changes in weight, body composition, biochemical indicators, calf circumference and handgrip strength were collected. Body fat and skeletal muscle were also measured using the bioelectrical impedance analysis. Paired t-test was used to compare the values after 6 months of treatment with the baseline values.

RESULTS

A total of 53 obese patients received 24 weeks of lifestyle intervention plus semaglutide treatment. 10 patients who failed to adhere to the follow-up were excluded, and 43 patients were studied. The average baseline body mass index (BMI) was 33.0 kg/m2, and the average body weight was 90.0 kg. After 6 months of treatment, the patient's weight was significantly reduced by 9.9 ± 3.9 kg (p < 0.001), and the weight loss percentage was 11.2 ± 4.5% (p< 0.001). The proportion of patients with weight loss ≥ 5% and ≥ 10% was 93% and 54%, respectively. Fasting blood glucose, fasting insulin, Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) index, blood uric acid and blood lipid levels also decreased after treatment. Body composition analysis showed that the loss of skeletal muscle mass was 1.4 ± 1.3 kg (p < 0.001), which was significantly less than the loss of fat mass of 5.6 ± 3.7 kg (p < 0.001). By percentage, the fat mass loss was 15.6 ± 10.1%, and the muscle mass loss was 4.8 ± 4.4% (p < 0.001). The visceral fat area was significantly reduced by 24.4 ± 17.7 cm (p < 0.001). There was no significant change in skeletal muscle index (8.1 ± 1.0 kg/m2 at baseline and 7.9 ± 1.0 kg/m2 at 24 weeks). The calf circumference (42.6 ± 3.6 cm at baseline, 41.2 ± 3.8 cm at 24 weeks) and grip strength (33.3 ± 9.5 kg at baseline, 32.3 ± 9.0 kg at 24 weeks) did not decrease significantly. The main adverse reactions were mild gastrointestinal dysfunction (nausea, diarrhea and vomiting), without ketoacidosis.

CONCLUSIONS

In a real-world setting, semaglutide can reduce the weight and fat of obese patients while effectively maintaining muscle mass and muscle strength.

Efficacy of percutaneous cement-augmented screw fixation plus percutaneous kyphoplasty in the management of unstable osteoporotic vertebral compression fractures

OBJECTIVE

The present study was performed to compare the efficacy of percutaneous kyphoplasty (PKP) vs. percutaneous cement-augmented screw fixation plus PKP in the management of unstable osteoporotic vertebral compression fractures (OVCF).

PATIENTS AND METHODS

A total of 197 patients with unstable OVCF treated in the Department of Spine Surgery, Lianyungang First People's Hospital from September 2019 to September 2021 were recruited and assigned via random number table method 1:1 to receive either PKP (group A, n=106) or PKP plus percutaneous cement-augmented screw fixation (group B, n=91). The outcome measures for the evaluation of different surgical methods included visual analogue scale (VAS), the height of the anterior-posterior border of the injured spine, Cobb angle of the posterior convexity, Oswestry disability index (ODI) scores, and Japanese Orthopaedic Association (JOA) scores.

RESULTS

PKP exhibited shorter operative time and length of hospital stay and less intraoperative blood loss vs. PKP plus percutaneous cement-augmented screw fixation (p<0.05). Patients with PKP plus percutaneous cement-augmented screw fixation experienced milder postoperative pain vs. those with PKP alone at 7 days postoperatively, as evidenced by the lower VAS scores (p<0.05). PKP plus percutaneous cement-augmented screw fixation provided more restoration of anterior margin height and posterior convexity Cobb angle vs. PKP alone (p<0.05). Patients with PKP only showed slightly higher Japanese Orthopaedic Association (JOA) scores than those with combined surgery, while the postoperative clinical signs between the two arms were similar (p>0.05).

CONCLUSIONS

Single PKP features the benefits of minimal trauma, simple operation, and rapid postoperative recovery in the treatment of OVCF. PKP plus percutaneous cement-augmented screw fixation for severe OVCF provided distinctly better performance than PKP alone in terms of early pain relief, restoration of vertebral body height, correction of posterior convexity deformity, and firm spinal stability.

Clinical prior Knowledge-Based One-Shot Learning for Automatic Delineation of Clinical Target Volumes in Adaptation Radiotherapy of Cervical Cancer

PURPOSE/OBJECTIVE(S)

Rapid and accurate delineation of clinical target volumes (CTV) of cervical cancer is the crux to ensure the efficiency and benefits of adaptation radiotherapy (ART). However, contour propagation using deformation image registration (DIR) is difficult to ensure the accuracy of CTV contours due to the significant tumor recession in next fraction, and the tumor progress in each fraction is not considered by conventional automatic delineation methods based on deep learning (DL). Currently, one-shot learning (OSL) is feasible to learn the tumor progress from former fractions to improve the accuracy of automatically delineating CTV.

MATERIALS/METHODS

We retrospectively collected 45 patients with cervical cancer from January 2021 to May 2022 in our department. All patients consist of a pair of planning CT and daily CT in ART. A personalized automatic delineation method based on one-shot learning was developed to delineate CTV in daily CT by learning the clinical prior knowledge from the CTV contours and images of planning CT. The performance of our proposed method was evaluated by dice similarity coefficient (DSC), 95% Harsdorff distance (95HD) and average surface distance (ASD) with human experts, and its automatic delineation performance were compared with DIR and DL in daily CT.

RESULTS

Our automatic delineation method OSL performed the best results in all evaluation metrics (denoted by mean ± standard deviation) as shown in Table 1, it is superior to method DL: 0.92 & 0.90 of DSC, 2.33 mm & 2.68 mm of HD95, 0.68 mm & 0.82 mm of ASD, P < 0.05 for DSC and ASD. Specifically, our method is significantly superior to the automatic delineation results by method DIR: 0.92 & 0.84 of DSC, 2.33 mm & 4.11 mm of HD95, 0.68 mm & 1.52 mm of ASD, P < 0.05 for all. In addition, OSL can significantly overcome the delineation problems in fuzzy boundary and delineation missing and perform better generalization for some unusual images, compared with DIR and DL.

CONCLUSION

We proposed an automatic delineation method based on one-shot learning for CTV of cervical cancer in ART, the results demonstrated that the proposed method could improve the precision and generalization of automatically delineating CTV compared against current popular methods. Therefore, it is potential to improve the quality and efficiency of ART for personalized patients and have a positive impact on tumor control and patient survival.

Measurement of the Dependence of the Hadron Production Fraction Ratios f_{s}/f_{u} and f_{d}/f_{u} on B Meson Kinematic Variables in Proton-Proton Collisions at sqrt[s]=13 TeV

The dependence of the ratio between the B_{s}^{0} and B^{+} hadron production fractions, f_{s}/f_{u}, on the transverse momentum (p_{T}) and rapidity of the B mesons is studied using the decay channels B_{s}^{0}→J/ψϕ and B^{+}→J/ψK^{+}. The analysis uses a data sample of proton-proton collisions at a center-of-mass energy of 13 TeV, collected by the CMS experiment in 2018 and corresponding to an integrated luminosity of 61.6  fb^{-1}. The f_{s}/f_{u} ratio is observed to depend on the B p_{T} and to be consistent with becoming asymptotically constant at large p_{T}. No rapidity dependence is observed. The ratio of the B^{0} to B^{+} meson production fractions, f_{d}/f_{u}, is also measured, for the first time in proton-proton collisions, using the B^{0}→J/ψK^{*0} decay channel. The result is found to be within 1 standard deviation of unity and independent of p_{T} and rapidity, as expected from isospin invariance.

[Clinical effects of expanded flaps in reconstructing scar contracture deformities in the face and neck after extensive burns]

Objective: To investigate the clinical effects of expanded flaps in reconstructing scar contracture deformities in the face and neck after extensive burns. Methods: A retrospective observational study was conducted. From May 2016 to September 2022, 17 patients with scar contracture deformities in the face and neck after extensive burns were admitted to Tongren Hospital of Wuhan University & Wuhan Third Hospital, including 13 males and 4 females, aged 23 to 55 years, with 3 patients having degree Ⅱ cervical contracture, 14 patients having degree Ⅲ cervical contracture, and 12 patients having facial scar contracture deformity. In the first stage, 34 rectangular skin and soft tissue expanders (hereinafter referred to as expanders) with rated capacity of 100-600 mL were inserted into the face, chest, shoulder, and abdomen, and then the normal saline was injected for expansion. In the second stage, the scar tissue was removed and the contracture was released to correct the deformity. Two expanded facial flaps were transplanted in local fashion, 17 expanded flaps were transplanted in pedicled fashion, and 15 expanded flaps were freely transplanted to repair the secondary wounds after release, with artery pressurization was performed in 7 flaps. Indocyanine green fluorescence imaging was used to evaluate the arterial blood perfusion and venous return of the flaps during transplantation. The incision area of 32 flaps except 2 facial flaps was 10 cm×8 cm-36 cm×16 cm. The wounds of 31 flap donor sites were closed by direct suture, and the wound of 1 flap donor site was repaired by autologous split-thickness scalp transplantation. The skin condition of inserted place, expansion time, and total amount of normal saline injection of expanders, complications of skin and soft tissue expansion surgery, and survival of flap after the second stage surgery were observed and recorded. The long-term face and neck reconstruction effect and recovery of flap donor area were followed up. At the last follow-up, the 5-level Likert scale was used to evaluate the efficacy satisfaction of patients. Results: Of the 34 expander inserted places in 17 patients, 22 places were superficial scar skin after deep partial-thickness burns, 8 places were superficial scar skin after multiple skin donations, and 4 places were normal skin. After 4 to 15 months of expansion, the total normal saline injection volume was 238 to 2 000 mL, with no complications occurred. After the second stage surgery, the distal part of 2 pedicled flaps was partially necrotic, and the necrotic wounds were healed after flap dressing and free transplantation of contralateral expanded triangular flaps, respectively; the other flaps survived completely. During 6 to 18 months of follow-up, except for 2 expanded paraumbilical flaps and 1 expanded groin flap, which were bloated and improved by flap thinning, the appearance and texture of the other flaps were good, and all the flap donor sites recovered well. At the last follow-up, the face and neck scar contracture deformities were significantly improved in all patients, and the satisfaction of curative effect of patient was very satisfactory in 8 patients and relatively satisfactory in 9 patients. Conclusions: The expanded flaps of chest, abdomen, and other parts, combined with local advance, pedicled, and free transplantation, can effectively reconstruct scar contracture deformities in the face and neck after extensive burns, restore the function of operative area and improve the appearance simultaneously, with high degree of patient satisfaction, which is worthy of promotion in clinic.

Application of aptamer-based viral detection in animals

Viral infections are common causes of diseases in animals and appropriate methods are increasingly being required to detect viral pathogens in animals. In this regard, similar to antigen- -antibody interactions, aptamers have high affinity and specificity for their respective target molecules, and can be selected using the Systematic Evolution of Ligands by EXponential enrichment (SELEX) technique. Recently, significant progress has been made in the development of aptamer selection and aptamer-based sensors for viral detection, and here we review some of the recent advances in aptamer-based detection of viral infections in animals. This review will serve as a comprehensive resource for aptamer-based strategies in viral diagnostics.

[Long-term high-fat diet's impact on synaptic plasticity in the visual cortex and hippocampus neurons: an experimental study]

Objective: To investigate the effects and mechanisms of long-term high-fat diet on synaptic plasticity in the visual cortex and hippocampus neurons of juvenile mice. Methods: This was an experimental study. Twenty-four 4-week-old male C57BL/6J mice were randomly divided into two groups, using a randomized numerical table, with 12 mice in each group. The ND group was fed a normal diet, while the HFD group was fed a high-fat diet. After 12 weeks of feeding, mouse body weight, body fat percentage, glucose tolerance, and blood lipid levels were recorded. Six mice from each group were randomly selected using a randomized numerical table, and long-term potentiation (LTP) in the lateral geniculate nucleus (LGN)-primary visual cortex binocular zone (V1B area) and hippocampus CA3-CA1 were recorded in vitro. Field excitatory postsynaptic potentials (fEPSPs) were measured, and the normalized fEPSP slope was calculated to evaluate changes in cortical synaptic plasticity. Subsequently, brain tissue was collected for Golgi staining to observe the development of pyramidal neurons in layers Ⅱ-Ⅲ of the primary visual cortex and CA1 region of the hippocampus, and changes in dendritic spine morphology and quantity were compared. The remaining six mice from each group were euthanized, and brain tissue was collected for transmission electron microscopy to observe ultrastructural changes in the visual cortex V1B area and hippocampus CA1 region neurons. Independent samples t-test was used for statistical analysis. Results: After 12 weeks of feeding, the body weight of mice in the HFD group was (29.17±1.63) g, significantly lower than the ND group which was (37.99±6.87) g (t=4.33, P<0.001). The body fat percentage in the HFD group was 1.09%±0.22%, which was higher than the ND group with 0.85%±0.09% (t=2.50, P=0.032). HFD mice showed a significant increase in blood glucose level 30 minutes after glucose injection, reaching (17.80±3.94) mmol/L, compared to the ND group with (23.10±1.48) mmol/L (t=3.07, P=0.013). At 60 minutes after glucose injection, the difference in blood glucose levels between the ND group [(13.58±2.39) mmol/L] and the HFD group [(23.70±3.56) mmol/L] was statistically significant (t=5.40, P<0.001). Subsequently, both groups showed a decline in blood glucose levels, and at 120 minutes after glucose injection, the blood glucose level in the ND group decreased to (8.50±1.05) mmol/L, while the HFD group remained at a higher level of (16.03±4.17) mmol/L, showing a statistically significant difference (t=3.91, P=0.004). The serum total cholesterol levels in the ND and HFD groups were (4.08±0.35) mmol/L and (10.80±0.90) mmol/L, respectively, with the HFD group higher than the ND group (t=15.23, P<0.001). However, there was no significant difference in triglyceride levels (P>0.05). The high-density lipoprotein cholesterol level in the ND group was (2.12±0.57) mmol/L, while in the HFD group, it was (1.28±0.15) mmol/L, with the HFD group lower than the ND group (t=3.15, P=0.014). Non-high-density lipoprotein cholesterol level in the HFD group was (11.06±1.46) mmol/L, significantly higher than the ND group with (2.28±0.43) mmol/L (t=12.88, P<0.001). In the hippocampal CA3-CA1 pathway, the fEPSP slope increased by 239.1%±88.8% of baseline in the ND group, while in the HFD group, it was only 147.6%±31.6% of baseline, indicating lower LTP compared to the ND group (t=7.20, P<0.001). For the LGN-V1 pathway, the fEPSP slope increased by 204.8%±67.0% of baseline in the ND group, while in the HFD group, it was 121.1%±15.7% of baseline, showing reduced LTP compared to the ND group (t=9.11, P<0.001). Regarding the visual cortex, in the V1B area of the ND group, the number of dendritic spines per 10 μm was (1.31±1.14), while in the HFD group, it was (0.77±0.43), demonstrating a significant decrease in dendritic spine density (t=3.45, P<0.001). The proportion of mature dendritic spines in the ND group was 69.98%, while non-mature dendritic spines accounted for 30.02%. In contrast, the HFD group had 45.76% mature dendritic spines and 54.24% non-mature dendritic spines. Regarding changes in hippocampal CA1 pyramidal neurons, the cell bodies and axons were not damaged, but HFD group neurons exhibited simplified dendritic structures with reduced branching. The number of dendritic spines per 10 μm was (10.25±3.84) in the HFD group and (25.22±8.21) in the ND group, indicating significantly lower dendritic spine density in the HFD group (t=12.42, P<0.001). The proportion of mature dendritic spines in the ND group was 70.88%, while non-mature dendritic spines accounted for 29.12%. In contrast, the HFD group had 47.37% mature dendritic spines and 52.63% non-mature dendritic spines. Moreover, the ultrastructure of neurons in the visual cortex V1B area and hippocampus CA1 region of HFD mice showed evident damage, with disrupted cell structures, swollen and vacuolated mitochondria, reduced or even disappeared mitochondrial cristae, and decreased synaptic quantity with damaged structure. Conclusions: Long-term high-fat diet in juvenile mice leads to abnormal development and functional maturation of synapses in the visual cortex and hippocampal regions. Dendrites, as the foundation of synaptic structures, undergo abnormal development, which can cause alterations in synaptic plasticity of related neural circuits.

Search for Exotic Higgs Boson Decays H→AA→4γ with Events Containing Two Merged Diphotons in Proton-Proton Collisions at sqrt[s]=13 TeV

We present the first direct search for exotic Higgs boson decays H→AA, A→γγ in events with two photonlike objects. The hypothetical particle A is a low-mass spin-0 particle decaying promptly to a merged diphoton reconstructed as a single photonlike object. We analyze the data collected by the CMS experiment at sqrt[s]=13  TeV corresponding to an integrated luminosity of 136  fb^{-1}. No excess above the estimated background is found. We set upper limits on the branching fraction B(H→AA→4γ) of (0.9-3.3)×10^{-3} at 95% confidence level for masses of A in the range 0.1-1.2 GeV.

[Short-term efficacy and safety observation of standardized mite allergen extract rush subcutaneous immunotherapy for allergic rhinitis: a prospective study]

Objective: To conduct a comparative analysis of the efficacy, safety, and cytokine changes associated with three distinct dose escalation regimens of allergen-specific immunotherapy (AIT), and to provide valuable insights into the implementation of safer and more effective accelerated immunotherapy in clinical practice. Methods: A prospective study of subcutaneous immunotherapy (SCIT) was conducted at Renmin Hospital of Wuhan University, involving patients with allergic rhinitis visited from 2019 to 2022. Participants were allocated to one of three treatment groups based on their preferences: conventional immunotherapy (CIT, 23 cases), cluster immunotherapy (CLIT, 25 cases), or rush immunotherapy (RIT, 18 cases). The RIT group received a single subcutaneous injection of 150 mg of omalizumab one week before commencing treatment. Subjective evaluation indices, including the Combined Symptom and Medication Score (CSMS), Visual Analogue Scale (VAS), and single symptom score, were recorded alongside objective evaluation indices (e.g., sIgE, tIgE, Th1/2 and Th17 cytokines) and adverse reactions. Assessments were conducted at baseline, and at 1, 7, and 15 weeks after treatment. SPSS 22.0 software was used for data processing and analysis. Results: The study included a total of 66 patients, comprising 37 males and 29 females, who completed the treatment regimen. In all three groups, CSMS and VAS scores showed significant reductions at 1, 7, and 15 weeks post-treatment (all P<0.05). Notably, the RIT group demonstrated a significantly lower VAS score (4.33±0.94) compared to the CIT (9.48±1.37) and CLIT (9.44±1.33) groups at 1 week post-treatment (P<0.05). Additionally, the RIT group (0.62±0.23) exhibited a lower CSMS score than the CIT (1.54±0.21) and CLIT (1.06±0.22) groups at 15 weeks post-treatment (P<0.05). Furthermore, at the point of reaching the maintenance dose, the RIT group (0.61±0.20) demonstrated superior improvement in nasal itching symptoms compared to the CIT (1.78±0.38) and CLIT groups (1.56±0.32), with P<0.05. The incidence of local adverse reactions in the RIT group (36/11.76%) was lower than that in the CIT (69/20.00%) and CLIT groups (62/16.53%), with P<0.05. Notably, none of the three groups reported grade 3/4 systemic adverse reactions, and there was no statistically significant difference in systemic adverse reactions among the three groups. Following treatment, IL-4, IL-5, IL-6, IL-17, sIgE, sIgE/tIgE, and Eos% exhibited varying degrees of decrease in all three groups, whereas IL-10, TNF, and IFN-γ did not show significant changes. Conclusions: All three distinct dose escalation regimens of SCIT demonstrated substantial clinical efficacy. Of note, the approach of combining a single injection of omalizumab with RIT significantly improved early-stage efficacy and exhibited the advantages of safety, effectiveness, and convenience, establishing it as a reliable immunotherapy method.