Precise Measurement of the e^{+}e^{-}→D_{s}^{*+}D_{s}^{*-} Cross Sections at Center-of-Mass Energies from Threshold to 4.95 GeV

The process e^{+}e^{-}→D_{s}^{*+}D_{s}^{*-} is studied with a semi-inclusive method using data samples at center-of-mass energies from threshold to 4.95 GeV collected with the BESIII detector operating at the Beijing Electron Positron Collider. The Born cross sections of the process are measured for the first time with high precision in this energy region. Two resonance structures are observed in the energy-dependent cross sections around 4.2 and 4.4 GeV. By fitting the cross sections with a coherent sum of three Breit-Wigner amplitudes and one phase-space amplitude, the two significant structures are assigned masses of (4186.8±8.7±30) and (4414.6±3.4±6.1)  MeV/c^{2}, widths of (55±15±53) and (122.5±7.5±8.1)  MeV, where the first errors are statistical and the second ones are systematic. The inclusion of a third Breit-Wigner amplitude is necessary to describe a structure around 4.79 GeV.

First Experimental Study of the Purely Leptonic Decay D_{s}^{*+}→e^{+}ν_{e}

Using 7.33  fb^{-1} of e^{+}e^{-} collision data taken with the BESIII detector at the BEPCII collider, we report the first experimental study of the purely leptonic decay D_{s}^{*+}→e^{+}ν_{e}. Our data contain a signal of this decay with a statistical significance of 2.9σ. The branching fraction of D_{s}^{*+}→e^{+}ν_{e} is measured to be (2.1_{-0.9_{stat}}^{+1.2}±0.2_{syst})×10^{-5}, corresponding to an upper limit of 4.0×10^{-5} at the 90% confidence level. Taking the total width of the D_{s}^{*+} [(0.070±0.028)  keV] predicted with the radiative D_{s}^{*+} decay from the lattice QCD calculation as input, the decay constant of the D_{s}^{*+} is determined to be f_{D_{s}^{*+}}=(214_{-46_{stat}}^{+61}±44_{syst})  MeV, corresponding to an upper limit of 354 MeV at the 90% confidence level.

Using Longitudinal MRI to Manage Proton Range Uncertainty for Pediatric Proton Craniospinal Irradiation

PURPOSE/OBJECTIVE(S)

Clinical evidence has shown that proton therapy can effectively reduce side effects for pediatric patients undergoing vertebral body-sparing craniospinal irradiation (VBS CSI), compared to conventional photon treatment modalities. However, radiation-induced growth impairment remains challenging for VBS CSI due to proton range uncertainty, compromising vertebral body sparing for growing children. Previous studies have shown that fatty marrow replacement can be observed in vertebral bodies 4-48 weeks after treatment is complete. This study aims to detect and quantify the fatty marrow replacement in vertebral bodies using longitudinal magnetic resonance (MR) to manage proton range uncertainty.

MATERIALS/METHODS

A prospective clinical trial of proton VBS CSI was designed, and ten pediatric patients were enrolled with prescribed doses of 15-36 Gy. The thecal sac and neural foramina were the clinical target volumes, and a Monte Carlo planning system was used to robustly optimize treatment plans with a 3.5% range margin. We analyzed patients' T1/T2 MR images acquired before, during, and after proton treatment to investigate the hematopoietic marrow transformation induced by irradiation. A metric was defined to calculate the ratio of fatty and hematopoietic marrow based on relative MR intensity histograms. We proposed a machine learning method via Gaussian fitting process (ML-GFP) to explore hidden correlations between marrow transition and radiation dose to 2 cm3 of the bone marrow (D2cc). We also leveraged this method to embed uncertainty to support potential proton range management for VBS enhancement.

RESULTS

The results indicated that fatty marrow replacement could be observed during inter-fractional treatment. For instance, an individual patient showed that the fatty marrow generation ratios were 0.54, 0.74, and 0.45, corresponding to 11, 18, and 65 days after the treatment started. Using ML-GFP, the fatty marrow transition was found to be quadratically correlated to treatment fractions, and the maximum transformation ranged from 40 to 50 days. Then marrow regeneration was observed due to the decrease in fatty marrow ratios. The fatty marrow ratios were also positively correlated to the D2cc doses ranging from 10 Gy to 36 Gy. Limited by insufficient low-dose data, the ML-GFP model extrapolated the data to predict the marrow transformation below 10 Gy.

CONCLUSION

We demonstrated the feasibility of using non-invasive longitudinal MR to quantify the fatty marrow transition from inter-fractional treatment. Based on this prospective study, the method can detect early fatty marrow generation in vertebrae caused by proton irradiation due to the conservative range margin used for robust optimization. The proposed method could be used to validate the actual proton range, allowing an accurate range margin to be defined to preserve bone marrow. Future investigation will likely focus on clinical implementation to improve life quality for pediatric CSI patients.

Voxel Based Analysis of Local Dose Patterns on Radiation Induced Xerostomia in Head and Neck Cancer with Machine Learning Methods

PURPOSE/OBJECTIVE(S)

Despite the technological advancements, xerostomia caused by the irradiation of parotid and/or submandibular glands significantly impacts a patient's quality of life and remains a clinical challenge. The mean dose within the salivary glands that neglects the local dose effect is an oversimplified predictor thus its efficacy is severely limited. This study aims to uncover the local dose patterns associated with the xerostomia by performing a voxel-based analysis of the spatial parotid dose.

MATERIALS/METHODS

A cohort of 143 patients who underwent intensity modulated radiation therapy treatment were retrospectively selected for this study. A binary classification was adopted for predictive modeling: group 1 (102 patients) if grade 0 or 1 and group 2 (41 patients) if grade 2 or 3. The anatomy of the parotid gland from the computed tomography (CT) of these patients were standardized to that of a reference patient with rigid and multistage B-spline deformable registration. The resultant deformation field vectors were subsequently utilized to warp the parotid doses and dose gradients to the reference ones. To facilitate pattern recognition, the bilateral parotid doses and dose gradients were further regrouped into contra- and ipsi-lateral depending on their proximity to the tumor targets. Four supervised machine learning (ML) models including ReliefF, L1 regularized logistic regression (L1LR), ridge regression (RR), and random forest (RF) were employed to determine the feature importance of each voxel and discover the dose patterns within the glands that are correlated with xerostomia. The predictive performance of each model was evaluated with the area under the curves (AUC) of the receiver operating characteristic.

RESULTS

The image registration accuracy of an average of 95.6% for the parotid gland standardization on CT images was achieved. The four ML models were assessed with 10-fold cross validation showing comparable performance and the AUC scores were 0.74 ± 0.09, 0.75 ± 0.08, 0.73 ± 0.10, and 0.77 ± 0.09 for ReliefF, L1LR, RR, and RF, respectively. The importance patterns showed that the doses to the superior and medial region of the contralateral parotid gland were more correlated with xerostomia from the ReliefF model but not others. Similar importance patterns were observed from the dose gradient distributions along the left-right and anterior-posterior directions.

CONCLUSION

The voxel-based model, which takes into account the spatial information of dose and dose gradient within the parotid gland, demonstrated an improvement in xerostomia predictive modeling as compared to the mean dose predictor, particularly in the local dose effect on xerostomia. If these findings are validated with more patient data and independent studies, they could be incorporated into treatment planning of head and neck patients to reduce the toxicity occurrence.

A Bibliometric Analysis for Low-Intensity Ultrasound Study Over the Past Three Decades

Low-intensity ultrasound (LI-US) is a non-invasive stimulation technique that has emerged in recent years and has been shown to have positive effects on neuromodulation, fracture healing, inflammation improvement, and metabolic regulation. This study reports the conclusions of a bibliometric analysis of LI-US. Input data for the period between 1995 and 2022, including 7209 related articles in the field of LI-US, were collected from the core library of the Web of Science (WOS) database. Using these data, a set of bibliometric indicators was obtained to gain knowledge on different aspects: global production, research areas, and sources analysis, contributions of countries and institutions, author analysis, citation analysis, and keyword analysis. This study combined the data analysis capabilities provided by the WOS database, making use of two bibliometric software tools: R software and VOS viewer to achieve analysis and data exploration visualization, and predicted the further development trends of LI-US. It turns out that the United States and China are co-leaders while Zhang ZG is the most significant author in LI-US. In the future, the hot spots of LI-US will continue to focus on parameter research, mechanism discussion, safety regulations, and neuromodulation applications.

Estimating Potential Benefits of Online Adaptive Proton Therapy for Head-and-Neck Cancer: A Retrospective Study

PURPOSE/OBJECTIVE(S)

Proton therapy is highly sensitive to anatomical changes and setup variations in head-and-neck (HN) treatments. To address this issue, proton centers often acquire patient CT images weekly to monitor patient anatomical changes during the treatment course and perform offline plan adaptation when needed. However, offline adaptation cannot fully account for daily setup variations or the anatomical changes occurring with high frequency. There are a few groups endeavoring to develop advanced technologies to enable online adaptive proton therapy (APT). However, the necessity of online APT remains controversial, as it is unknown that whether online APT will significantly improve treatment quality and outcomes compared to offline APT. The purpose of this study is to estimate the clinical potential of online APT in the management of HN cancers in relation to the current offline APT.

MATERIALS/METHODS

Our retrospective study was conducted with four HN patients (35 fractions per patient), who had been treated with intensity modulated proton therapy and had offline adaptation once or twice during their treatment courses. Synthetic CT (sCT) images were generated from 140 daily CBCT images for us to recalculate the dose of the treatment plan in patient's actual treatment anatomy for each treatment fraction and adapt the plan when warranted. These adaptations were assumed to be performed online before treatment delivery to mimic an online APT course. Accumulative doses were calculated for both courses using the CBCT-based sCT images of every fraction for us to compare the target coverage, organ at risk (OAR) sparing, tumor control probability (TCP) and normal tissue complication probability (NTCP). An in-house script was developed to semi-automate this process in a commercial treatment planning system to facilitate our study.

RESULTS

All patients would benefit from online APT to different extents. For the first patient, with OAR doses comparable to the actual offline course, the retrospective online APT course improved dose coverages of the three CTVs from 95.2%, 98.64% and 89.53% to 98.88%, 99.81%, 98.97%, which would lead to a 4.52% improvement in TCP. Similarly, online APT would yield a 2.66% improvement in TCP for the second patient. For the third patient, with comparable CTV dose coverages, the mean doses of right parotid and oral cavity were decreased from 29.52 Gy relative biological effectiveness (RBE) and 41.89 Gy RBE to 22.16 Gy RBE and 34.61 Gy RBE, leading to a reduce of 1.67% and 3.40% in NTCP. The mean dose of right parotid was decreased from 21.71 Gy RBE to 19.37 Gy RBE for the last patient, leading to a reduce of 0.73% in NTCP.

CONCLUSION

Our results showed that online APT could better maintain the treatment plan quality than offline APT for all the four patients, despite their significant anatomical changes. Future investigation will focus on collecting more patient data to obtain statistically significant results and help identify the patients to whom the online APT will be of most benefit.

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.

Multi-Criteria Optimization and RapidPlan for Improved Organs at Risk Sparing in Treatment Planning of Malignant Pleural Mesothelioma

PURPOSE/OBJECTIVE(S)

Treatment planning for malignant pleural mesothelioma is complex and time-consuming owing to the large volume of the target as well as its overlap and proximity to critical organs. Knowledge-based planning (KBP) model using RapidPlan (RP) was previously developed and clinically tested at our institution for treatment planning of these cases that had undergone pleurectomy-decortication, and therefore had two intact-lungs. The aim of this work is to investigate if multi-criteria optimization (MCO) can further improve the RP model with respect to organ at risk (OAR) sparing without compromising target coverage.

MATERIALS/METHODS

The RP model was trained with clinically accepted plans of 57 patients that used Volumetric Modulated Arc Therapy (VMAT) with 2 partial arcs and 6 MV photons. The dose volume histogram (DVH) estimation model was trained to estimate doses to the heart, ipsilateral lung, total lung, contralateral lung, stomach, esophagus, kidneys and liver. Clinical treatment plans for 12 patients were re-planned using RP as well as a combination of RP and MCO. Application of MCO was after RP and used tradeoff exploration to navigate to the improved dose distribution on a Pareto surface. Selection of a plan that further improved OAR sparing while maintaining coverage constraints of PTV D95 and V95 ≥ 94% was made. Dosimetric parameters for clinical plans (CP), plans using RP as well as plans generated with RP and MCO were all compared for 12 new validation cases. Wilcoxon sign-rank test was used for statistical significance testing.

RESULTS

The mean heart dose was reduced from 20.9 Gy ± 3.2 Gy for CP to 17.1 Gy ± 4.2 Gy with RP and further to 13.8 Gy ± 3.3 Gy with RP and MCO. The heart V30 Gy was reduced from 23.7% ± 8% to 18.2% ± 9.4% with RP and further to 13.6% ± 5.7% with RP and MCO. The corresponding results for contralateral lung V5 Gy were 68.6% ± 14.1% with CP, 50% ± 26.3% with RP and 45.3% ± 22.8% with RP and MCO, while the mean esophagus dose was reduced from 26.5 Gy ± 3.1 Gy to 21.5 Gy ± 4.7 Gy with RP and further to 19.1 Gy ± 4.2 Gy with RP and MCO. All these dosimetric improvements were statistically significant (p<0.001). However, improvements with RP and MCO for the total lung mean and V20 Gy, liver mean, stomach mean and kidney V18 Gy were marginal over the RP. Sparing of ipsilateral lung V20 Gy was maintained at ≥ 50 cc on average for all plans. PTV D95 and V95 were both normalized at 94%.

CONCLUSION

Combination of RP and MCO significantly improved sparing of the OARs, especially the heart, contralateral lung and esophagus without compromising coverage or doses to other structures. Since doses to the heart, and contralateral lung are known to correlate with incidence of radiation pneumonitis, it would be prudent to consider planning with both RP and MCO to help determine the optimal treatment plan for the individual patient's anatomy.

10 Prism adaptation testing in children to improve surgical outcomes in esotropia surgery

The aim of strabismus surgery is to improve alignment and ideally restore binocular vision. Prism adaptation (PAT) in esotropia is known to improve post-operative outcomes by allowing choice of target angle in line with pre-operative fusion potential. Reaching a successful end-point in PAT requires co-operation, which is not a given in the paediatric population. The purpose of this study is to describe the successful use of prism adaptation in children in planning target angles for esotropia surgery.Retrospective review of 29 cases from a tertiary centre was conducted, including cases who underwent PAT prior to esotropia surgery over a 6-year period (Jan 2016- Jan 2023). Data was collected from the electronic patient record including patient age, diagnosis, measurements of presenting and prism adapted angles, surgery performed and surgical outcomes.Successful prism adaptation was carried out in children of ages 3-16 . The average presenting angle of esodeviation was 20.9 PD in the distance and 27.3 PD at near. The average maximum prism adapted angle, which was the target angle used for surgery, was 35.3 PD (range 25-50PD). All patients underwent bimedial recessions except one, a re-do who underwent LR re-advancement. 3 patients had one LR resection in addition (3/28). 89.7% of patients achieved binocular vision with stereopsis, 3.4% achieved binocular vision with simultaneous perception. 2 patients had no demonstrable BV, neither were overcorrected post-operatively.Pre-operative PAT is an effective tool in pre-operative assessment of paediatric esotropia patients with binocular potential and can be used to optimise surgical planning and outcomes.

Self-reported sleep disorders and the risk of all cancer types: evidence from the Kailuan Cohort study

OBJECTIVES

Previous studies that focussed on sleep disturbance have primarily examined specific aspects of sleep disorders rather than considering overall sleep quality. We aimed to investigate different sleep disorders and their combination as risk factors for different types of cancer.

STUDY DESIGN

Prospective cohort study.

METHODS

In this prospective cohort study, we included 78,232 participants. A self-reported questionnaire was used to address insomnia, daytime sleepiness, snoring, and sleep duration. Overall sleep quality was evaluated by summarising these four sleep parameters. Cox proportional hazards analysis was used to estimate the hazard ratios and their 95% confidence intervals for determining the effect of the overall sleep-quality score and its components on the risk of incident cancer.

RESULTS

During a median follow-up of 5.67 years, 1266 participants were diagnosed with incident cancer. Compared to participants in the best sleep-quality score group, participants in the worst sleep-quality score group had a higher subsequent risk of overall cancer, and colorectal, breast, uterine or uterine cervical, prostatic, kidney, and bladder cancer. Participants with insomnia and snoring status had an elevated risk of head and neck, breast, uterine or uterine cervical, prostatic, kidney, bladder cancer, and lymphoma.

CONCLUSIONS

Poor overall sleep-quality scores as well as poor scores for the scale's components, including insomnia and snoring status, elevated the risk of overall and several specific-site cancers.

TRIAL REGISTRATION

Kailuan Study, ChiCTR2000029767. Registered 12 February, 2020-Retrospectively registered, https://www.chictr.org.cn/showprojEN.html?proj=48316.

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.

A Hybrid Intelligence (HI) System for Segmenting Rectoprostatic Spacer Gel and Key OARs on CT Images for Prostate Cancer Radiation Therapy Planning

PURPOSE/OBJECTIVE(S)

Our hybrid intelligence (HI) system, combining natural and artificial intelligence, is effective for auto-contouring H&N and thorax organs at risk (OARs) for radiation therapy (RT) planning with FDA 510(k) clearance. The purpose of this study is to test the HI system to segment a commercially available retroprostatic hyaluronic acid spacer gel (RSG) and pelvic OARs in planning CT images for prostate cancer RT.

HYPOTHESIS

HI can achieve clinically acceptable auto segmentation for tissue-equivalent RSG in this domain.

MATERIALS/METHODS

RSG is injected in the peri-rectal space in men with prostate cancer prior to RT to minimize rectal toxicity. 190 patients with prostate cancer were included in this post-hoc image analysis from a multi-center, prospective, randomized trial, with 136 in the spacer arm. The HI system has 3 steps: rough recognition from fuzzy model (FM) based automatic anatomy recognition (AAR-R), deep learning-based recognition (DL-R) refinement, and deep learning-based delineation (DL-D) to contour objects guided by the recognition results. FM encodes high level 3D anatomy knowledge of object shape and its relationship with other OARs; DL-R and DL-D focus on pixel-level details. The 190 studies are divided into disjoint training (100) and testing (90) subsets. 100 samples are used in DL-R and DL-D training, with 45 to build the FM for AAR-R. RSG and 4 other OARs (pelvic skin, prostate, bladder, rectum) are contoured. Location error (LE) is used to evaluate recognition; Dice coefficient (DC) and Hausdorff distance (HD) are employed to evaluate delineation. Acceptability scores (AS) (range 1-5, 1 for poor quality, 5 for best quality) from an observer study are recorded for HI-output and ground truth masks of RSG for assessing segmentation quality.

RESULTS

The HI system achieves highest DC (0.94±0.07) and lowest HD (1.96±1.61 mm) for bladder, for rectum and prostate similar DC (0.82±0.08) and HD (2.62±1.65mm), for RSG, the most challenging object, a good DC close to 0.7 (0.67±0.10) and excellent HD (2.66±1.44mm). AS for auto-segmentations (3.86±0.85) were significantly better than those for ground truth segmentations (3.45±1.00) (p = 0.02, paired t-test). Table 1 summarizes results.

CONCLUSION

The HI system achieves clinically acceptable segmentations for pelvic OARs and significantly better acceptability of segmentation of RSG compared to clinically performed ground truth segmentations. This has implications in improving efficiency and accuracy of CT-based RT planning in patients with prostate cancer.

Modelling enzyme inhibition toxicity of ionic liquid from molecular structure via convolutional neural network model

Deep learning (DL) methods further promote the development of quantitative structure-activity/property relationship (QSAR/QSPR) models by dealing with complex relationships between data. An acetylcholinesterase inhibitory toxicity model of ionic liquids (ILs) was established using a convolution neural network (CNN) combined with support vector machine (SVM), random forest (RF) and multilayer perceptron (MLP). A CNN model was proposed for feature self-learning and extraction of ILs. By comparing with the model results through feature engineering (FE), the model regression results based on the CNN model for feature extraction have been substantially improved. The results showed that all six models (FE-SVM, FE-RF, FE-MLP, CNN-SVM, CNN-RF, and CNN-MLP) had good prediction accuracy, but the results based on the CNN model were better. The hyperparameters of six models were optimized by grid search and the 10-fold cross validation. Compared with the existing models in the literature, the model performance has been further improved. The model could be used as an intelligent tool to guide the design or screening of low-toxicity ILs.

The Role of Radiotherapy in Patients with Refractory Hodgkin Lymphoma after Brentuximab Vedotin and -/or Immune Checkpoint Inhibitors

PURPOSE/OBJECTIVE(S)

Brentuximab vedotin (BV) and immune checkpoint inhibitors (ICIs) had important roles in the treatment of relapse or refractory (R/R) Hodgkin lymphoma (HL). Treatment of refractory disease after BV and -/or ICIs remains a challenge. This study was conducted to evaluate the efficacy and safety of radiotherapy for R/R HL after failure to BV or ICIs.

MATERIALS/METHODS

We retrospectively analyzed patients in two institutions with R/R HL who had failed after first-line therapy, and were refractory to BV or ICIs, and received radiotherapy (RT) thereafter. The overall response rate (ORR), duration of response (DOR), progression-free survival (PFS) and overall survival (OS) were analyzed.

RESULTS

A total of 19 patients were enrolled. First-line systemic therapy consisted of ABVD (84.2%), AVD + ICIs (10.5%) and BEACOPP (5.3%), respectively. After first-line therapy, 15 patients (78.9%) were refractory, and 4 patients (21.1%) relapsed. After diagnosis of R/R HL, 8 patients (42.1%) received BV, and 17 patients (89.5%) received ICIs. RT was delivered in all 19 patients who failed after BV or ICIs. In 16 efficacy-evaluable patients, the ORR and CR rate were 100% and 100%. The median DOR was 17.2 months (range, 7.9 to 46.7 months). 3 patients progressed at outside of the radiation field. The in-field-response rate was 100%. The 12-month PFS and OS were 84.4% and 100%, respectively. No patients were reported with sever adverse events.

CONCLUSION

This study concluded that radiotherapy was effective and safe for refractory HL after BV or ICIs. Further prospective studies were warranted.

Expression of FOXO3 in the skin follicles of goose embryos during embryonic development

1. The Forkhead box O3 (FOXO3) transcription factor is a crucial regulator in controlling cell metabolism, proliferation, apoptosis, migration and response to oxidative stress. However, FOXO3 has not previously been studied much in the embryonic skin follicles of geese.2. This study used Zhedong white geese (Anser cygnoides), Jilin white geese (Anser cygnoides) and Hungarian white geese (Anser anser). The feather follicle structure in the dorsal skin during embryonic stages was examined with haematoxylin and eosin (HE) and Pollak staining. The FOXO3 protein content in the embryonic dorsal skin from feather follicles was detected using western blotting and quantitative real-time PCR.3. The mRNA expression level of FOXO3 in the dorsal skin of Jilin white geese was highly expressed on embryonic day 23 (E23; P < 0.01), while mRNA expression of FOXO3 was highly expressed in the feather follicle of Hungarian white geese at E28 (P < 0.01). The expression of FOXO3 protein mainly concentrated in the early embryonic phase among these goose breeds (P < 0.05). This suggested that FOXO3 plays a crucial role in the development and growth of embryonic dorsal skin of feather follicles. The location of the FOXO3 protein was determined using the IHC technique, which further verified the effect of FOXO3 in the dorsal skin for feather follicles during embryogenesis.4. The study demonstrated the differential expression and localisation of the FOXO3 gene among different goose species. It was speculated that the gene could potentially improve goose feather follicle development and feather-related traits and provide a basis for further understanding of FOXO3 function in the dorsal tissue of goose embryos.

Search for Λ[over ¯]-Λ Baryon-Number-Violating Oscillations in the Decay J/ψ→pK^{-}Λ[over ¯]+c.c

We report on the first search for Λ[over ¯]-Λ oscillations in the decay J/ψ→pK^{-}Λ[over ¯]+c.c. by analyzing 1.31×10^{9}  J/ψ events accumulated with the BESIII detector at the BEPCII collider. The J/ψ events are produced using e^{+}e^{-} collisions at a center of mass energy sqrt[s]=3.097  GeV. No evidence for hyperon oscillations is observed. The upper limit for the oscillation rate of Λ[over ¯] to Λ hyperons is determined to be P(Λ)=[B(J/ψ→pK^{-}Λ+c.c.)/B(J/ψ→pK^{-}Λ[over ¯]+c.c.)]<4.4×10^{-6} corresponding to an oscillation parameter δm_{ΛΛ[over ¯]} of less than 3.8×10^{-18}  GeV at the 90% confidence level.

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.

[Diagnosis and treatment of hereditary cholestasis]

With China's outstanding achievements in the prevention and treatment of hepatitis, hereditary cholestasis caused by genetic variants has gradually become an important cause of death or transplantation in children with liver disease. The continuous identification of new pathogenic genes expands the disease spectrum and clinician's understanding of disease. The disease characteristics and clinical manifestations of hereditary cholestasis caused by different gene variants vary, and the severity of diseases caused by the same gene variants and the response to treatment are also significantly different. Therefore, early genetic diagnosis is of great value for improving the clinical management of patients. In terms of treatment, in addition to traditional drugs and surgery, targeted therapy and gene therapy are also gradually moving towards clinical application. Advances in metabolomics, gene editing technology, and structural biology have made it possible to provide personalized and precise treatment of children with hereditary cholestasis in the future; however, this which will put forward higher requirements for on relevant practitioners.

Development of a Brief Cognitive Screening Tool for Predicting Postoperative Delirium in Patients with Parkinson's Disease: A Secondary Analysis

Background

A simple, rapid, and effective cognitive screening test appropriate for fast-paced settings with limited resources and staff is essential, especially preoperatively. This study aimed to develop and validate the short versions of Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) for predicting postoperative delirium (POD) in patients with Parkinson's disease (PD) who were scheduled for surgery.

Methods

The current study was a secondary analysis of data collected from 128 inpatients scheduled for deep brain stimulation of the subthalamic nuclei (STN-DBS) lasting >60 min, at Tsinghua University Yuquan Hospital, China. Preoperative cognitive screening was performed during the preoperative visit using the MMSE and MoCA. The optimal MMSE and MoCA cut-off scores for detecting PD-MCI was 27 and 23 respectively. The POD was assessed twice a day on the first postoperative day until discharge by the confusion assessment method. The backward conditional logistic regression analysis was used to organize the reduced versions of the MMSE or MoCA. Also, the areas under the receiver operating characteristic curves (AUCs) were examined using the DeLong test.

Results

125/128 PD patients were included in the analysis, and 27 (21.6%) developed POD. The MMSE reduced version (orientation to time, attention and calculation, and comprehension) demonstrated performance similar to the original MMSE in predicting POD (z=0.820, p=0.412). The AUC of the original MoCA and the short MoCA (visuospatial and executive attention and orientation) were 0.808 and 0.826, respectively. There was no significantly difference in the AUC values between the tests (z=0.561, p=0.575).

Conclusion

Our simplified MMSE and MoCA could be efficiently used to identify patients at risk for POD. Also, short cognitive tests could be considered while predicting POD in fast-paced preoperative settings with limited resources and staff.

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.

A Nomogram for Predicting Progression-free Survival in Patients with Endometrial Cancer

AIMS

Endometrial cancer is one of the most widely known gynaecological malignancies that lacks a prognostic prediction model. This study aimed to develop a nomogram to predict progression-free survival (PFS) in patients with endometrial cancer.

MATERIALS AND METHODS

Information for endometrial cancer patients diagnosed and treated from 1 January 2005 to 30 June 2018 was collected. The Kaplan-Meier survival analysis and multivariate Cox regression analysis were carried out to determine the independent risk factors and a nomogram was constructed by R based on analytical factors. Internal and external validation were then carried out to predict the probability of 3- and 5-year PFS.

RESULTS

In total, 1020 patients with endometrial cancer were included in the study and the relationship between 25 factors and prognosis was analysed. Postmenopause (hazard ratio = 2.476, 95% confidence interval 1.023-5.994), lymph node metastasis (hazard ratio = 6.242, 95% confidence interval 2.815-13.843), lymphovascular space invasion (hazard ratio = 4.263, 95% confidence interval 1.802-10.087), histological type (hazard ratio = 2.713, 95% confidence interval 1.374-5.356), histological differentiation (hazard ratio = 2.601, 95% confidence interval 1.141-5.927) and parametrial involvement (hazard ratio = 3.596, 95% confidence interval 1.622-7.973) were found to be independent prognostic risk factors; these factors were selected to establish a nomogram. The consistency index for 3-year PFS were 0.88 (95% confidence interval 0.81-0.95) in the training cohort and 0.93 (95% confidence interval 0.87-0.99) in the verification set. The areas under the receiver operating characteristic curve for the 3- and 5-year PFS predictions are 0.891 and 0.842 in the training set; the same conclusion also appeared in the verification set [0.835 (3-year), 0.803(5-year)].

CONCLUSIONS

This study established a prognostic nomogram for endometrial cancer that provides a more individualised and accurate estimation of PFS for patients, which will help physicians make follow-up strategies and risk stratification.

Observation of Same-Sign WW Production from Double Parton Scattering in Proton-Proton Collisions at sqrt[s]=13 TeV

The first observation of the production of W^{±}W^{±} bosons from double parton scattering processes using same-sign electron-muon and dimuon events in proton-proton collisions is reported. The data sample corresponds to an integrated luminosity of 138  fb^{-1} recorded at a center-of-mass energy of 13 TeV using the CMS detector at the CERN LHC. Multivariate discriminants are used to distinguish the signal process from the main backgrounds. A binned maximum likelihood fit is performed to extract the signal cross section. The measured cross section for production of same-sign W bosons decaying leptonically is 80.7±11.2(stat) _{-8.6}^{+9.5}(syst)±12.1(model)  fb, whereas the measured fiducial cross section is 6.28±0.81(stat)±0.69(syst)±0.37(model)  fb. The observed significance of the signal is 6.2 standard deviations above the background-only hypothesis.

Observation of the Rare Decay of the η Meson to Four Muons

A search for the rare η→μ^{+}μ^{-}μ^{+}μ^{-} double-Dalitz decay is performed using a sample of proton-proton collisions, collected by the CMS experiment at the CERN LHC with high-rate muon triggers during 2017 and 2018 and corresponding to an integrated luminosity of 101  fb^{-1}. A signal having a statistical significance well in excess of 5 standard deviations is observed. Using the η→μ^{+}μ^{-} decay as normalization, the branching fraction B(η→μ^{+}μ^{-}μ^{+}μ^{-})=[5.0±0.8(stat)±0.7(syst)±0.7(B_{2μ})]×10^{-9} is measured, where the last term is the uncertainty in the normalization channel branching fraction. This work achieves an improved precision of over 5 orders of magnitude compared to previous results, leading to the first measurement of this branching fraction, which is found to agree with theoretical predictions.

Erratum: Global Polarization of Ξ and Ω Hyperons in Au+Au Collisions at sqrt[s_{NN}]=200 GeV [Phys. Rev. Lett. 126, 162301 (2021)]

This corrects the article DOI: 10.1103/PhysRevLett.126.162301.

[Tyro3 and CDK9 as biomarkers for drug resistance to breast cancer anti-PD-1 therapies]

Objective: PD-1/PD-L1 immune checkpoint treatment is effective for some triple-negative breast cancer populations with PD-L1 expression, but the response rate is still not satisfactory. This study aims to explore the mechanism of drug resistance to breast cancer anti-PD-1 therapies and the strategies for overcoming the resistance to PD-1therapies. Methods: By constructing a human triple-negative breast cancer drug-resistant cell line called BT-549R5 and a mouse breast cancer drug-resistant cell line called 4T1R3, and applying the whole-gene shRNA library screening, candidate drug resistance-associated molecules were obtained and verified by cytological experiments. The expression of Tyro3, Axl and MerTK of the TAM family in the 4T1R3 group was tested using the Western blot method. The down-regulation of CDK9 on the effect of T cells killing the BT-549R5 cells was observed through T cell killing tests, while the down-regulation of Tyro3 and CDK9 on the effect of anti-PD-1 therapies for transplanted breast tumors was observed in mouse tumor formation experiments. Results: The cell lines and animal models of breast cancer resistant to PD-1 treatment were successfully constructed. Tyro3, Axl and MerTK were highly expressed in 4T1R3 cells. Whole genome sequencing showed that Tyro3 and CDK9 were highly expressed in BT-549R5 cells. T cell killing experiment showed that the survival rate of BT-549R5 cells in the CDK9 down-regulated group and the control group decreased gradually with the increase of T cells, but the survival rate of BT-549R5 cells in the CDK9 down-regulated group decreased rapidly. Tumor formation experiment in mice showed that under anti-PD-1 treatment, the transplanted tumor in the 4T1R3 cell group grew rapidly compared with the 4T1 cell group (P<0.05), and the tumor volume of the 4T1R3 group was larger than that of the 4T1 group on Day 20. Nevertheless, the tumor growth rates in the CDK9-knockdown 4T1R3 cell group and the Tyro3-knockdown 4T1R3 cell group were similar to that of the 4T1 cell group, and the tumor volumes at day 20 were signiference lower than that of 4T1R3 cell group(P<0.05). Conclusions: Tyro3 and CDK9 are associated with the drug resistance to anti-PD-1 therapies for breast cancer. Inhibiting the expression of Tyro3 and CDK9 can reverse the drug resistance to breast cancer treatment.