A multi-modal vision-language pipeline strategy for contour quality assurance and adaptive optimization

Objective.Accurate delineation of organs-at-risk (OARs) is a critical step in radiotherapy. The deep learning generated segmentations usually need to be reviewed and corrected by oncologists manually, which is time-consuming and operator-dependent. Therefore, an automated quality assurance (QA) and adaptive optimization correction strategy was proposed to identify and optimize 'incorrect' auto-segmentations.Approach.A total of 586 CT images and labels from nine institutions were used. The OARs included the brainstem, parotid, and mandible. The deep learning generated contours were compared with the manual ground truth delineations. In this study, we proposed a novel contour quality assurance and adaptive optimization (CQA-AO) strategy, which consists of the following three main components: (1) the contour QA module classified the deep learning generated contours as either accepted or unaccepted; (2) the unacceptable contour categories analysis module provided the potential error reasons (five unacceptable category) and locations (attention heatmaps); (3) the adaptive correction of unacceptable contours module integrate vision-language representations and utilize convex optimization algorithms to achieve adaptive correction of 'incorrect' contours.Main results. In the contour QA tasks, the sensitivity (accuracy, precision) of CQA-AO strategy reached 0.940 (0.945, 0.948), 0.962 (0.937, 0.913), and 0.967 (0.962, 0.957) for brainstem, parotid and mandible, respectively. The unacceptable contour category analysis, the(FI,AccI,Fmicro,Fmacro)of CQA-AO strategy reached (0.901, 0.763, 0.862, 0.822), (0.855, 0.737, 0.837, 0.784), and (0.907, 0.762, 0.858, 0.821) for brainstem, parotid and mandible, respectively. After adaptive optimization correction, the DSC values of brainstem, parotid and mandible have been improved by 9.4%, 25.9%, and 13.5%, and Hausdorff distance values decreased by 62%, 70.6%, and 81.6%, respectively.Significance. The proposed CQA-AO strategy, which combines QA of contour and adaptive optimization correction for OARs contouring, demonstrated superior performance compare to conventional methods. This method can be implemented in the clinical contouring procedures and improve the efficiency of delineating and reviewing workflow.

Rethinking automatic segmentation of gross target volume from a decoupling perspective

Accurate and reliable segmentation of Gross Target Volume (GTV) is critical in cancer Radiation Therapy (RT) planning, but manual delineation is time-consuming and subject to inter-observer variations. Recently, deep learning methods have achieved remarkable success in medical image segmentation. However, due to the low image contrast and extreme pixel imbalance between GTV and adjacent tissues, most existing methods usually obtained limited performance on automatic GTV segmentation. In this paper, we propose a Heterogeneous Cascade Framework (HCF) from a decoupling perspective, which decomposes the GTV segmentation into independent recognition and segmentation subtasks. The former aims to screen out the abnormal slices containing GTV, while the latter performs pixel-wise segmentation of these slices. With the decoupled two-stage framework, we can efficiently filter normal slices to reduce false positives. To further improve the segmentation performance, we design a multi-level Spatial Alignment Network (SANet) based on the feature pyramid structure, which introduces a spatial alignment module into the decoder to compensate for the information loss caused by downsampling. Moreover, we propose a Combined Regularization (CR) loss and Balance-Sampling Strategy (BSS) to alleviate the pixel imbalance problem and improve network convergence. Extensive experiments on two public datasets of StructSeg2019 challenge demonstrate that our method outperforms state-of-the-art methods, especially with significant advantages in reducing false positives and accurately segmenting small objects. The code is available at https://github.com/shijun18/GTV_AutoSeg.

Deep learning for head and neck semi-supervised semantic segmentation

Objective. Radiation therapy (RT) represents a prevalent therapeutic modality for head and neck (H&N) cancer. A crucial phase in RT planning involves the precise delineation of organs-at-risks (OARs), employing computed tomography (CT) scans. Nevertheless, the manual delineation of OARs is a labor-intensive process, necessitating individual scrutiny of each CT image slice, not to mention that a standard CT scan comprises hundreds of such slices. Furthermore, there is a significant domain shift between different institutions' H&N data, which makes traditional semi-supervised learning strategies susceptible to confirmation bias. Therefore, effectively using unlabeled datasets to support annotated datasets for model training has become a critical issue for preventing domain shift and confirmation bias.Approach. In this work, we proposed an innovative cross-domain orthogon-based-perspective consistency (CD-OPC) strategy within a two-branch collaborative training framework, which compels the two sub-networks to acquire valuable features from unrelated perspectives. More specifically, a novel generative pretext task cross-domain prediction (CDP) was designed for learning inherent properties of CT images. Then this prior knowledge was utilized to promote the independent learning of distinct features by the two sub-networks from identical inputs, thereby enhancing the perceptual capabilities of the sub-networks through orthogon-based pseudo-labeling knowledge transfer.Main results. Our CD-OPC model was trained on H&N datasets from nine different institutions, and validated on the four local intuitions' H&N datasets. Among all datasets CD-OPC achieved more advanced performance than other semi-supervised semantic segmentation algorithms.Significance. The CD-OPC method successfully mitigates domain shift and prevents network collapse. In addition, it enhances the network's perceptual abilities, and generates more reliable predictions, thereby further addressing the confirmation bias issue.

Neuroprotection of low dose carbon monoxide in Parkinson's disease models commensurate with the reduced risk of Parkinson's among smokers

Paradoxically, cigarette smoking is associated with a reduced risk of Parkinson's disease (PD). This led us to hypothesize that carbon monoxide (CO) levels, which are constitutively but modestly elevated in smokers, might contribute to neuroprotection. Using rodent models of PD based on α-synuclein (αSyn) accumulation and oxidative stress, we show that low-dose CO mitigates neurodegeneration and reduces αSyn pathology. Oral CO administration activated signaling cascades mediated by heme oxygenase-1 (HO-1), which have been implicated in limiting oxidative stress, and in promoting αSyn degradation, thereby conferring neuroprotection. Consistent with a neuroprotective effect of smoking, HO-1 levels in cerebrospinal fluid were higher in human smokers compared to nonsmokers. Moreover, in PD brain samples, HO-1 levels were higher in neurons without αSyn pathology. Thus, CO in rodent PD models reduces pathology and increases oxidative stress responses, phenocopying possible protective effects of smoking evident in PD patients. These data highlight the potential for low-dose CO modulated pathways to slow symptom onset and limit pathology in PD patients.

Integration of dosimetric parameters, clinical factors, and radiomics to predict symptomatic radiation pneumonitis in lung cancer patients undergoing combined immunotherapy and radiotherapy

PURPOSE

This study aimed to combine clinical/dosimetric factors and handcrafted/deep learning radiomic features to establish a predictive model for symptomatic (grade ≥ 2) radiation pneumonitis (RP) in lung cancer patients who received immunotherapy followed by radiotherapy.

MATERIALS AND METHODS

This study retrospectively collected data of 73 lung cancer patients with prior receipt of ICIs who underwent thoracic radiotherapy (TRT). Of these 73 patients, 41 (56.2 %) developed symptomatic grade ≥ 2 RP. RP was defined per multidisciplinary clinician consensus using CTCAE v5.0. Regions of interest (ROIs) (from radiotherapy planning CT images) utilized herein were gross tumor volume (GTV), planning tumor volume (PTV), and PTV-GTV. Clinical/dosimetric (mean lung dose and V5-V30) parameters were collected, and 107 handcrafted radiomic (HCR) features were extracted from each ROI. Deep learning-based radiomic (DLR) features were also extracted based on pre-trained 3D residual network models. HCR models, Fusion HCR model, Fusion HCR + ResNet models, and Fusion HCR + ResNet + Clinical models were built and compared using the receiver operating characteristic (ROC) curve with measurement of the area under the curve (AUC). Five-fold cross-validation was performed to avoid model overfitting.

RESULTS

HCR models across various ROIs and the Fusion HCR model showed good predictive ability with AUCs from 0.740 to 0.808 and 0.740-0.802 in the training and testing cohorts, respectively. The addition of DLR features improved the effectiveness of HCR models (AUCs from 0.826 to 0.898 and 0.821-0.898 in both respective cohorts). The best performing prediction model (HCR + ResNet + Clinical) combined HCR & DLR features with 7 clinical/dosimetric characteristics and achieved an average AUC of 0.936 and 0.946 in both respective cohorts.

CONCLUSIONS

In patients undergoing combined immunotherapy/RT for lung cancer, integrating clinical/dosimetric factors and handcrafted/deep learning radiomic features can offer a high predictive capacity for RP, and merits further prospective validation.

Dosimetric predictors of radiation pneumonitis in patients with prior immunotherapy exposure: A multi-institutional analysis

BACKGROUND AND PURPOSE

Combining immune checkpoint inhibitors (ICIs) and thoracic radiotherapy (TRT) may magnify the radiation pneumonitis (RP) risk. Dosimetric parameters can predict RP, but dosimetric data in context of immunotherapy are very scarce. To address this knowledge gap, we performed a large multicenter investigation to identify dosimetric predictors of RP in this under-studied population.

MATERIALS AND METHODS

All lung cancer patients from five institutions who underwent conventionally-fractionated thoracic intensity-modulated radiotherapy with prior ICI receipt were retrospectively compiled. RP was defined per CTCAE v5.0. Statistics utilized logistic regression modeling and receiver operating characteristic (ROC) analysis.

RESULTS

The vast majority of the 192 patients (median follow-up 14.7 months) had non-small cell lung cancer, received PD-1 inhibitors, and did not receive concurrent systemic therapy with TRT. Grades 1-5 RP occurred in 21.9%, 25.0%, 8.3%, 1.6%, and 1.0%, respectively. The mean MLD for patients with grades 1-5 RP was 10.7, 11.6, 12.6, 14.7, and 12.8 Gy, respectively. On multivariable analysis, tumor location and mean lung dose (MLD) significantly predicted for any-grade and grade ≥ 2 pneumonitis. Only MLD significantly predicted for grade ≥ 3 RP. ROC analysis was able to pictorially model RP risk probabilities for a variety of MLD thresholds, which can be an assistive tool during TRT treatment planning.

CONCLUSION

This study, by far the largest to date of dosimetric predictors of RP in the immunotherapy era, illustrates that MLD is the most critical dose-volume parameter influencing RP risk. These data may provide a basis for revising lung dose constraints in efforts to better prevent RP in this rapidly expanding ICI/TRT population.

Accurate and robust auto-segmentation of head and neck organ-at-risks based on a novel CNN fine-tuning workflow

PURPOSE

Obvious inconsistencies in auto-segmentations exist among various AI software. In this study, we have developed a novel convolutional neural network (CNN) fine-tuning workflow to achieve precise and robust localized segmentation.

METHODS

The datasets include Hubei Cancer Hospital dataset, Cetuximab Head and Neck Public Dataset, and Québec Public Dataset. Seven organs-at-risks (OARs), including brain stem, left parotid gland, esophagus, left optic nerve, optic chiasm, mandible, and pharyngeal constrictor, were selected. The auto-segmentation results from four commercial AI software were first compared with the manual delineations. Then a new multi-scale lightweight residual CNN model with an attention module (named as HN-Net) was trained and tested on 40 samples and 10 samples from Hubei Cancer Hospital, respectively. To enhance the network's accuracy and generalization ability, the fine-tuning workflow utilized an uncertainty estimation method for automatic selection of candidate samples of worthiness from Cetuximab Head and Neck Public Dataset for further training. The segmentation performances were evaluated on the Hubei Cancer Hospital dataset and/or the entire Québec Public Dataset.

RESULTS

A maximum difference of 0.13 and 0.7 mm in average Dice value and Hausdorff distance value for the seven OARs were observed by four AI software. The proposed HN-Net achieved an average Dice value of 0.14 higher than that of the AI software, and it also outperformed other popular CNN models (HN-Net: 0.79, U-Net: 0.78, U-Net++: 0.78, U-Net-Multi-scale: 0.77, AI software: 0.65). Additionally, the HN-Net fine-tuning workflow by using the local datasets and external public datasets further improved the automatic segmentation with the average Dice value by 0.02.

CONCLUSION

The delineations of commercial AI software need to be carefully reviewed, and localized further training is necessary for clinical practice. The proposed fine-tuning workflow could be feasibly adopted to implement an accurate and robust auto-segmentation model by using local datasets and external public datasets.

Attention Connect Network for Liver Tumor Segmentation from CT and MRI Images

Introduction: Currently, the incidence of liver cancer is on the rise annually. Precise identification of liver tumors is crucial for clinicians to strategize the treatment and combat liver cancer. Thus far, liver tumor contours have been derived through labor-intensive and subjective manual labeling. Computers have gained widespread application in the realm of liver tumor segmentation. Nonetheless, liver tumor segmentation remains a formidable challenge owing to the diverse range of volumes, shapes, and image intensities encountered. Methods: In this article, we introduce an innovative solution called the attention connect network (AC-Net) designed for automated liver tumor segmentation. Building upon the U-shaped network architecture, our approach incorporates 2 critical attention modules: the axial attention module (AAM) and the vision transformer module (VTM), which replace conventional skip-connections to seamlessly integrate spatial features. The AAM facilitates feature fusion by computing axial attention across feature maps, while the VTM operates on the lowest resolution feature maps, employing multihead self-attention, and reshaping the output into a feature map for subsequent concatenation. Furthermore, we employ a specialized loss function tailored to our approach. Our methodology begins with pretraining AC-Net using the LiTS2017 dataset and subsequently fine-tunes it using computed tomography (CT) and magnetic resonance imaging (MRI) data sourced from Hubei Cancer Hospital. Results: The performance metrics for AC-Net on CT data are as follows: dice similarity coefficient (DSC) of 0.90, Jaccard coefficient (JC) of 0.82, recall of 0.92, average symmetric surface distance (ASSD) of 4.59, Hausdorff distance (HD) of 11.96, and precision of 0.89. For AC-Net on MRI data, the metrics are DSC of 0.80, JC of 0.70, recall of 0.82, ASSD of 7.58, HD of 30.26, and precision of 0.84. Conclusion: The comparative experiments highlight that AC-Net exhibits exceptional tumor recognition accuracy when tested on the Hubei Cancer Hospital dataset, demonstrating highly competitive performance for practical clinical applications. Furthermore, the ablation experiments provide conclusive evidence of the efficacy of each module proposed in this article. For those interested, the code for this research article can be accessed at the following GitHub repository: https://github.com/killian-zero/py_tumor-segmentation.git.

Deep learning for fast super-resolution ultrasound microvessel imaging

Objective. Ultrasound localization microscopy (ULM) enables microvascular reconstruction by localizing microbubbles (MBs). Although ULM can obtain microvascular images that are beyond the ultimate resolution of the ultrasound (US) diffraction limit, it requires long data processing time, and the imaging accuracy is susceptible to the density of MBs. Deep learning (DL)-based ULM is proposed to alleviate these limitations, which simulated MBs at low-resolution and mapped them to coordinates at high-resolution by centroid localization. However, traditional DL-based ULMs are imprecise and computationally complex. Also, the performance of DL is highly dependent on the training datasets, which are difficult to realistically simulate.Approach. A novel architecture called adaptive matching network (AM-Net) and a dataset generation method named multi-mapping (MMP) was proposed to overcome the above challenges. The imaging performance and processing time of the AM-Net have been assessed by simulation andin vivoexperiments.Main results. Simulation results show that at high density (20 MBs/frame), when compared to other DL-based ULM, AM-Net achieves higher localization accuracy in the lateral/axial direction.In vivoexperiment results show that the AM-Net can reconstruct ∼24.3μm diameter micro-vessels and separate two ∼28.3μm diameter micro-vessels. Furthermore, when processing a 128 × 128 pixels image in simulation experiments and an 896 × 1280 pixels imagein vivoexperiment, the processing time of AM-Net is ∼13 s and ∼33 s, respectively, which are 0.3-0.4 orders of magnitude faster than other DL-based ULM.Significance. We proposes a promising solution for ULM with low computing costs and high imaging performance.

In-Situ Growth of High-Quality Single-Crystal Twisted Bilayer Graphene on Liquid Copper

Twisted bilayer graphene (TBG) has generated significant attention in the fundamental research of two-dimensional (2D) materials due to its distinct twist-angle-dependent properties. Exploring the efficient production of TBG with a wide range of twist angles stands as one of the major frontiers in moiré materials. Here, we reported the local space-confined chemical vapour deposition growth technique for high-quality single-crystal TBG with twist angles ranging from 0 to 30° on liquid copper substrates. The clean surface, pristine interface, high crystallinity, and thermal stability of TBG were verified by using comprehensive characterization techniques including optical microscopy, and electron microscopy, secondary-ion mass spectrometry. The proportion of TBG in bilayer graphene reached as high as 89%. In addition, we investigated the stacking structure and growth mechanism of TBG, revealing that the second graphene layer developed beneath the first one. A series of comparative experiments illustrated that the liquid copper surface, with its excellent fluidity, promotes the growth of TBG. Electrical measurements showed the twist-angle-dependent electronic properties of as-grown TBG, achieving a room-temperature carrier mobility of 26640 cm2 V-1 s-1 . This work provides an approach for the in-situ preparation of 2D twisted materials and facilitates the application of TBG in the fields of electronics. This article is protected by copyright. All rights reserved.

PCG-net: feature adaptive deep learning for automated head and neck organs-at-risk segmentation

Introduction

Radiation therapy is a common treatment option for Head and Neck Cancer (HNC), where the accurate segmentation of Head and Neck (HN) Organs-AtRisks (OARs) is critical for effective treatment planning. Manual labeling of HN OARs is time-consuming and subjective. Therefore, deep learning segmentation methods have been widely used. However, it is still a challenging task for HN OARs segmentation due to some small-sized OARs such as optic chiasm and optic nerve.

Methods

To address this challenge, we propose a parallel network architecture called PCG-Net, which incorporates both convolutional neural networks (CNN) and a Gate-Axial-Transformer (GAT) to effectively capture local information and global context. Additionally, we employ a cascade graph module (CGM) to enhance feature fusion through message-passing functions and information aggregation strategies. We conducted extensive experiments to evaluate the effectiveness of PCG-Net and its robustness in three different downstream tasks.

Results

The results show that PCG-Net outperforms other methods, improves the accuracy of HN OARs segmentation, which can potentially improve treatment planning for HNC patients.

Discussion

In summary, the PCG-Net model effectively establishes the dependency between local information and global context and employs CGM to enhance feature fusion for accurate segment HN OARs. The results demonstrate the superiority of PCGNet over other methods, making it a promising approach for HNC treatment planning.

Deep learning for fast denoising filtering in ultrasound localization microscopy

Objective.Addition of a denoising filter step in ultrasound localization microscopy (ULM) has been shown to effectively reduce the error localizations of microbubbles (MBs) and achieve resolution improvement for super-resolution ultrasound (SR-US) imaging. However, previous image-denoising methods (e.g. block-matching 3D, BM3D) requires long data processing times, making ULM only able to be processed offline. This work introduces a new way to reduce data processing time through deep learning.Approach.In this study, we propose deep learning (DL) denoising based on contrastive semi-supervised network (CS-Net). The neural network is mainly trained with simulated MBs data to extract MB signals from noise. And the performances of CS-Net denoising are evaluated in bothin vitroflow phantom experiment andin vivoexperiment of New Zealand rabbit tumor.Main results.Forin vitroflow phantom experiment, the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of single microbubble image are 26.91 dB and 4.01 dB, repectively. Forin vivoanimal experiment , the SNR and CNR were 12.29 dB and 6.06 dB. In addition, single microvessel of 24μm and two microvessels separated by 46μm could be clearly displayed. Most importantly,, the CS-Net denoising speeds forin vitroandin vivoexperiments were 0.041 s frame-1and 0.062 s frame-1, respectively.Significance.DL denoising based on CS-Net can improve the resolution of SR-US as well as reducing denoising time, thereby making further contributions to the clinical real-time imaging of ULM.

A Ferroptosis Microneedle Integrated Wireless Implanted Photodynamic Therapy Pellet for Cancer Treatment

PURPOSE/OBJECTIVE(S)

Effective, non-toxic, and targeted induction of lung cancer cell death is urgently needed. The goal of this research is to create a new implantable battery-free therapeutic pellet with integrated drug microneedles that allows for wireless photodynamic therapy (PDT) and targeted release of a ferroptosis inducer (Imidazole ketone erastin, IKE) into tumor tissue.

MATERIALS/METHODS

A wireless power unit, μ-LED illuminant, a flexible control circuit, and an IKE-stored biodegradable microneedle enclosed in polydimethylsiloxane (PDMS) were all built into an integrated therapeutic pellet. Lung cancer cells were used to illustrate the in vitro viability and molecular biological processes of this system. Therapeutic pellet implanted into the LLC xenograft C57BL/6 model. PDT was conducted by 660 nm laser irradiation after injecting a photosensitizer (Chlorin e6, Ce6) and targeted IKE released into the tumor. Systematically analyzing the therapeutic effects on lung cancer and toxic side-effects.

RESULTS

The PDT-IKE group reduced cellular viability by 90% compared to the control group at the cellular level. In mouse model studies, the PDT-IKE group suppressed tumors at 78.8%, three or four times greater than the PDT (26.6%) or IKE (19.2%) group alone. The PDT-IKE group also controlled IKE release more precisely with heated electrodes, reducing nephrotoxicity and improving safety. Moreover, the combination of PDT and IKE can effectively cause ferroptosis in tumor cells, both in vivo and in vitro.

CONCLUSION

A new implantable battery-free therapeutic pellet was designed for wireless PDT with integrated IKE microneedles to induce obvious ferroptosis in lung cancer. The proposed pellet would provide a promising strategy for cancer treatment.

Controllable Synthesis and Growth Mechanism of Interlayer-Coupled Multilayer Graphene

The potential applications of multilayer graphene in many fields, such as superconductivity and thermal conductivity, continue to emerge. However, there are still many problems in the growth mechanism of multilayer graphene. In this paper, a simple control strategy for the preparation of interlayer-coupled multilayer graphene on a liquid Cu substrate was developed. By adjusting the flow rate of a carrier gas in the CVD system, the effect for finely controlling the carbon source supply was achieved. Therefore, the carbon could diffuse from the edge of the single-layer graphene to underneath the layer of graphene and then interlayer-coupled multilayer graphene with different shapes were prepared. Through a variety of characterization methods, it was determined that the stacked mode of interlayer-coupled multilayer graphene conformed to AB-stacking structure. The small multilayer graphene domains stacked under single-layer graphene was first found, and the growth process and growth mechanism of interlayer-coupled multilayer graphene with winged and umbrella shapes were studied, respectively. This study reveals the growth mechanism of multilayer graphene grown by using a carbon source through edge diffusion, paving the way for the controllable preparation of multilayer graphene on a liquid Cu surface.

[Electroencephalographic microstates in vestibular schwannoma patients with tinnitus]

OBJECTIVE

To explore the biomarkers of tinnitus in vestibular schwannoma patients using electroencephalographic (EEG) microstate technology.

METHODS

The EEG and clinical data of 41 patients with vestibular schwannoma were collected. All the patients were evaluated by SAS, SDS, THI and VAS scales. The EEG acquisition time was 10-15 min, and the EEG data were preprocessed and analyzed using MATLAB and EEGLAB software package.

RESULTS

Of the 41 patients with vestibular schwannoma, 29 patients had tinnitus and 12 did not have tinnitus, and their clinical parameters were comparable. The average global explanation variances of the non-tinnitus and tinnitus groups were 78.8% and 80.1%, respectively. The results of EEG microstate analysis showed that compared with those without tinnitus, the patients with tinnitus had an increased frequency (P=0.033) and contribution (P=0.028) of microstate C. Correlation analysis showed that THI scale scores of the patients were negatively correlated with the duration of microstate A (R=-0.435, P=0.018) and positively with the frequencies of microstate B (R=0.456, P=0.013) and microstate C (R=0.412, P=0.026). Syntax analysis showed that the probability of transition from microstate C to microstate B increased significantly in vestibular schwannoma patients with tinnitus (P=0.031).

CONCLUSION

EEG microstate features differ significantly between vestibular schwannoma patients with and without tinnitus. This abnormality in patients with tinnitus may reflect the potential abnormality in the allocation of neural resources and the transition of brain functional activity.

Achieving Stimuli-Responsive Amorphous Organic Afterglow in Single-Component Copolymer through Self-Doping

The development of stimuli-responsive materials with afterglow emission is highly desirable but remains a formidable challenge in a single-component material system. Herein, we propose a strategy to achieve photoactivated afterglow emission in a variety of amorphous copolymers through self-doping, endowed by the synergetic effect of self-host-induced guest sensitization and thermal-processed polymer rigidification for boosting the generation and stabilization of triplet excitons. Upon continuous ultraviolet illumination for regulating the oxygen concentration, a photoactivated afterglow showing increased lifetimes from 0.34 to 867.4 ms is realized. These afterglow emissions can be naturally or quickly deactivated to the pristine state under ambient conditions or heating treatment. Interestingly, programmable and reusable afterglow patterns, conceptual pulse-width indicators, and "excitation-time lock" Morse code are successfully established using stimuli-responsive afterglow polymers as recorded media. These findings offer an avenue to construct a single-component polymeric system with photoactivated organic afterglow features and demonstrate the superiority of stimuli-responsive materials for remarkable applications.

Machine Learning-Based Quality Assurance for Automatic Segmentation of Head-and-Neck Organs-at-Risk in Radiotherapy

Purpose/Objective(s): With the development of deep learning, more convolutional neural networks (CNNs) are being introduced in automatic segmentation to reduce oncologists' labor requirement. However, it is still challenging for oncologists to spend considerable time evaluating the quality of the contours generated by the CNNs. Besides, all the evaluation criteria, such as Dice Similarity Coefficient (DSC), need a gold standard to assess the quality of the contours. To address these problems, we propose an automatic quality assurance (QA) method using isotropic and anisotropic methods to automatically analyze contour quality without a gold standard. Materials/Methods: We used 196 individuals with 18 different head-and-neck organs-at-risk. The overall process has the following 4 main steps. (1) Use CNN segmentation network to generate a series of contours, then use these contours as organ masks to erode and dilate to generate inner/outer shells for each 2D slice. (2) Thirty-eight radiomics features were extracted from these 2 shells, using the inner/outer shells' radiomics features ratios and DSCs as the input for 12 machine learning models. (3) Using the DSC threshold adaptively classified the passing/un-passing slices. (4) Through 2 different threshold analysis methods quantitatively evaluated the un-passing slices and obtained a series of location information of poor contours. Parts 1-3 were isotropic experiments, and part 4 was the anisotropic method. Result: From the isotropic experiments, almost all the predicted values were close to the labels. Through the anisotropic method, we obtained the contours' location information by assessing the thresholds of the peak-to-peak and area-to-area ratios. Conclusion: The proposed automatic segmentation QA method could predict the segmentation quality qualitatively. Moreover, the method can analyze the location information for un-passing slices.

[Comparison of the therapeutic effects of optic nerve sheath fenestration and medication on papilledema due to cerebral venous thrombosis]

Objective: To compare the therapeutic effects of optic nerve sheath fenestration (ONSF) and medication on papilledema induced by cerebral venous thrombosis (CVT). Methods: Patients with papilledema induced by CVT in Beijing Tiantan Hospital and Xuanwu Hospital from January 2017 to July 2022 were retrospectively enrolled and were divided into two groups according to the treatment strategies they underwent, with 76 cases (107 eyes) in ONSF group and 35 cases (69 eyes) in medication group. The degree of papilledema was evaluated by the modified Frisén's grading (grade 0-1 was defined as mild, grade 2-3 was moderate, and grade 4-5 was severe edema). The mean defect (MD) of visual field, the degree of papilledema, and the mean retinal nerve fiber layer (RNFL) thickness in different subgroups were compared between baseline versus 1 month after ONSF or medication. Results: There were 76 cases in ONSF group (26 males and 50 females), and aged (35.3±11.4) years. Meanwhile, there were 35 cases in medication group (22 males and 13 females), and aged (35.2±11.0) years. Compared with baseline, MD were improved in both moderate [(-8.4±6.6) vs (-11.8±8.6) db, P=0.021] and severe [(-8.1±5.3) vs (-11.4±6.9) db, P<0.001] papilledema subgroups after ONSF, while there was an improvement in mild papilledema subgroup [(-1.5±5.3) vs (-3.4±5.1) db, P<0.001] after medication. The papilledema (Frisén's scores) in both ONSF group (P<0.001) and medication group (P=0.010) was improved. Compared with baseline, the mean RNFL decreased in mild [(78.5±13.5) vs (91.0±17.4) μm, P=0.002], moderate [(126.6±67.6) vs (154.8±77.9) μm, P=0.011] and severe [(179.0±70.9) vs (230.6±89.7) μm, P=0.001] papilledema subgroups after ONSF, while the mean RNFL decreased [(142.0±29.3) vs (158.8±22.7) μm, P=0.020] in moderate papilledema subgroup after medication. Conclusions: ONSF might attenuate CVT-mediated papilledema, and improve the visual function in patients with moderate and severe papilledema. Likewise, patients with mild papilledema could also get benefit from medication.

Multicolor hyperafterglow from isolated fluorescence chromophores

High-efficiency narrowband emission is always in the central role of organic optoelectronic display applications. However, the development of organic afterglow materials with sufficient color purity and high quantum efficiency for hyperafterglow is still great challenging due to the large structural relaxation and severe non-radiative decay of triplet excitons. Here we demonstrate a simple yet efficient strategy to achieve hyperafterglow emission through sensitizing and stabilizing isolated fluorescence chromophores by integrating multi-resonance fluorescence chromophores into afterglow host in a single-component copolymer. Bright multicolor hyperafterglow with maximum photoluminescent efficiencies of 88.9%, minimum full-width at half-maximums (FWHMs) of 38 nm and ultralong lifetimes of 1.64 s under ambient conditions are achieved. With this facilely designed polymer, a large-area hyperafterglow display panel was fabricated. By virtue of narrow emission band and high luminescent efficiency, the hyperafterglow presents a significant technological advance in developing highly efficient organic afterglow materials and extends the domain to new applications.

The Anti-Inflammatory Effect of miR-140-3p in BMSCs-Exosomes on Osteoarthritis

PURPOSE OF THE STUDY Articular cartilage injury is a common disease in daily life, with a high incidence. The aim of this study was to investigate the effect and mechanism of miRNA-140-3p in bone mesenchymal stem cells (BMSCs)-derived exosomes under hypoxia on inflammatory articular chondrocytes. MATERIAL AND METHODS To simulate the pathological status of arthritis, rat chondrocytes were used to establish the osteoarthritis (OA) model by IL-1β (10 μg/ml) as a modulating in vitro, and exosomes were isolated by differential ultra-high speed centrifugation. The cell counting kit-8, wound healing and flow cytometry assays were utilized to assess proliferation, migration and apoptosis of chondrocytes, respectively. Lipogenic and chondrogenic differentiation of chondrocytes were detected by oil red O staining and toluidine blue staining individually. The expressions of miR-140-3p and chondrocyte-specific gene mRNA were investigated using qRT-PCR. Western blot was applied to assess chondrocyte associated proteins and BMSC-Exo surface protein markers, and immunohistochemistry was adopted to detect the staining of collagen I and II. RESULTS Under scanning electronic microscope, the shape of exosomes was almost round. Exosome treatment prominently impaired the inhibition of chondrocytes' proliferative and migrative ability by IL-1β. It was found hypoxia had a more marked impact on proliferation, expression of collagen II and apoptosis in OA chondrocytes than normoxia, as well as a stronger effect on weakening adipose differentiation and enhancing chondrogenic differentiation in inflammatory chondrocytes. Furthermore, incubation with BMSC-Exo overexpressing miR-140-3p can remarkably increase the survival rate and migration in inflammatory chondrocytes. In addition, overexpression of miR-140-3p was found to enhance the chondrogenic differentiation of inflammatory chondrocytes. Furthermore, we found that the healing effect of exosomes on inflammatory chondrocytes under hypoxic conditions was produced by a rise in miR-140-3p expression within them and that hypoxia-mediated upregulation of miR-140-3p expression occurred through HIF-1α. CONCLUSIONS Under hypoxia, BMSC-Exo enhanced the chondrogenic phenotype, increased the viability of inflammatory chondrocytes. The overexpression of miR-140-3p in BMSC-Exo is beneficial to protect joints and delaying the pathogenesis in OA. Key words: HIF-1α, apoptosis, lipogenic differentiation, chondrogenic differentiation.

[Analysis of epidemiological characteristics of pulmonary tuberculosis patients in Fengtai District, Beijing City from 2011 to 2021]

Objective: To analyze the epidemiological characteristics of pulmonary tuberculosis (PTB) in Fengtai District from 2011 to 2021. Methods: A retrospective study was conducted, the data of PTB patients in Fengtai District from 2011 to 2021 were collected in Chinese disease prevention and Control Information System, which included etiological classification, gender, age, occupation, onset time, demographic information etc. the epidemiological characteristics of reported PTB patients was analysis. Results: A total of 10 342 cases of PTB were reported from 2011 to 2021 in Fengtai District, with an average annual reported incidence rate of 42.87/ 100 000. The incidence rate was the highest in 2012(75.89/100 000), and significantly declined from 2013, which declined to 29.70/100 000 in 2017. It showed a slow rise from 2018 to 2021. The difference was statistically significant (χ²=1 471.77,P<0.001).There were 2 975 cases of etiologic positive PTB from 2011 to 2021, and 76 cases of Rifampicin-resistant PTB from 2017 to 2021. The ratio of male cases to female was 1.75, the average annual incidence rate of male (53.94/100 000) was higher, than female(31.57/100 000).(χ²=704.01,P<0.001). Among all age groups, 25-29 years group, 20-24 years group and 30-34 years group had the highest proportion, which were 1 506 cases (14.56%) , 1 292 cases (12.49%) and 1 024 cases (9.90%) respectively. The average annual incidence rate was the lowest in the group less than 10 years old (1.43/100 000), and the highest in the group 85 years old and over (195.20/100 000), the difference was statistically significant(χ²=3164.24, P<0.001). The top occupations from high to low were housework and unemployment (2 917 cases, 28.21%), retirees (2 308 cases, 22.32%), workers (1 047 cases, 10.12%), cadres and staff (950 cases, 9.19%), farmers (860 cases, 8.32%), business services (698 cases, 6.75%), teachers and students (455 cases, 4.40%). Conclusion: From 2011 to 2021, the incidence rate of PTB was decreased from 2012 to 2017, and slowly increased lately in Fengtai District. The epidemiological characteristics of PTB vary in different age and gender.

Multi-elements characteristic and potential risk of heavy metals in MOUTAN CORTEX from Anhui Province, China

To ensure the quality and safety of herbs, the content of 54 elements in MOUTAN CORTEX (MC) was determined by the ICP-AES and ICP-MS, and the health risks of Cu, As, Cd, Pb, Hg and rare earth elements (REEs) were assessed. These herbs were collected from 5 producing areas in Anhui Province, China, namely Wuhu, Tongling, Bozhou, Xuancheng and Chizhou. The multi-elements fingerprint identification of MC in Anhui Province was established. The total amount of macro-elements from Wuhu and Tongling is significantly lower than Bozhou. Among all MC from 5 producing areas, the highest content is Ca. Except for Bozhou, the content of macro-elements and REES in the other 4 origins of MC is from highest to lowest: Ca > K > Mg > Al > Fe > Na and Ce > La > Nd > Y > Pr > Er > Yb > Eu > Ho > Tb > Tm > Lu. The chemical forms of Cd in MC from Bozhou with the highest percentage were PH2O of high toxicity and migration, while the other 4 regions were PNaCl of low activity and mobility. There was a great difference in the content of inorganic elements and chemical forms of Cd between the MC produced from the plain (Bozhou) and the hilly areas (Wuhu, Tongling, Chizhou and Xuancheng). Except for Cd, the content of Cu, As, Pb and Hg in MC did not exceed the limit. The results of PTWIFact and ADI for Cd and REEs showed that MC herbs did not pose a risk to human health.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13762-022-04402-6.

Automatic Segmentation of Target Structures for Total Marrow and Lymphoid Irradiation in Bone Marrow Transplantation

The use of total marrow and lymphoid irradiation (TMLI) as part of conditioning regimens for bone marrow transplantation is trending due to its advantages in disease control and low toxicity. Accurate contouring of target structures such as bone and lymph nodes plays an important role in irradiation planning. However, this process is often time-consuming and prone to inter-observer variation. Recently, deep learning methods such as convolutional neural networks (CNNs) and vision transformers have achieved tremendous success in medical image segmentation, therefore enabling fast semiautomatic radiotherapy planning. In this paper, we propose a dual-encoder U-shaped model named DE-Net, to automatically segment the target structures for TMLI. To enhance the learned features, the encoder of DE-Net is composed of parallel CNNs and vision transformers, which can model both local and global contexts. The multi-level features from the two branches are progressively fused by intermediate modules, therefore effectively preserving low-level details. Our experiments demonstrate that the proposed method achieves state-of-the-art results and a significant improvement in lymph node segmentation compared with existing methods.

Muscle Area and Density Assessed by Abdominal Computed Tomography in Healthy Adults: Effect of Normal Aging and Derivation of Reference Values

BACKGROUND

A growing body of evidence has demonstrated the prognostic value of skeletal muscle area and quality measured by computed tomography (CT) as biomarkers of sarcopenia and frailty. However, there exists little data in normal healthy subjects to inform reference values and determine the effects of advancing age and sex on CT muscle parameters.

METHODS

Abdominal CT images of patients (20-80 years of age) presenting to the emergency department with benign abdominal symptoms and no significant medical comorbidities were retrospectively collected from 2014 to 2017. Psoas and abdominal wall muscle area (PMA, WMA) and density (PMD, WMD) at the level of the L4 vertebrae were measured with the CoreSlicer.com web app. The normal reference range was computed by non-parameteric 2.5th and 97.5th percentiles stratified by sex and restricted by age to the younger subgroup (20-39 years of age).

RESULTS

The cohort consisted of 390 otherwise healthy patients (162 males, 228 females). The lower reference range for PMA was <22.0 cm2 in males and <11.1 cm2 in females, and for WMA was <112.2 cm2 in males and <75.6 cm2 in females. There was a graded decline observed in PMA and WMA among older compared to younger adults (especially ≥60 years of age) (P<0.001) and among females compared to males (P<0.001). There was also a graded decline observed in PMD and WMD among older compared to younger adults (P<0.001), irrespective of sex.

CONCLUSION

This study has defined the normal reference values and age-associated down-trend for CT muscle parameters at L4 in a healthy population using an accessible web-based software, which help contextualize and interpret these imaging biomarkers of sarcopenia in clinical care.

Resonance-Induced Stimuli-Responsive Capacity Modulation of Organic Ultralong Room Temperature Phosphorescence

Organic ultralong room temperature phosphorescence (OURTP) materials having stimuli-responsive attributes have attracted great attention due to their great potential in a wide variety of advanced applications. It is of fundamental importance but challengeable to develop stimuli-responsive OURTP materials, especially such materials with modulated optoelectronic properties in a controlled manner probably due to the lack of an authentic construction approach. Here, we propose an effective strategy for OURTP materials with controllably regulated stimuli-responsive properties by engineering the resonance linkage between flexible chain and phosphor units. A quantitative parameter to demonstrate the stimuli-responsive capacity is also established by the responsivity rate constant. The designed OURTP materials demonstrate efficient photoactivated OURTP with lifetimes up to 724 ms and tunable responsivity rate constants ranging from 0.132 to 0.308 min^-1 upon continuous UV irradiation. Moreover, the applications of stimuli-responsive resonance OURTP materials have been illustrated by the rewritable paper for snapshot and Morse code for multiple information encryption. Our works, which enable the accomplishment of OURTP materials capable of on-demand manipulated optical properties, demonstrate a viable design to explore smart OURTP materials, giving deep insights into the dynamically stimuli-responsive process.

Dosimetric Risk Factors for Acute Radiation Pneumonitis in Patients With Prior Receipt of Immune Checkpoint Inhibitors

Purpose

Dosimetric parameters (e.g., mean lung dose (MLD), V20, and V5) can predict radiation pneumonitis (RP). Constraints thereof were formulated before the era of combined immune checkpoint inhibitors (ICIs) and radiotherapy, which could amplify the RP risk. Dosimetric predictors of acute RP (aRP) in the context of ICIs are urgently needed because no data exist thus far.

Methods and Materials

All included patients underwent thoracic intensity-modulated radiotherapy, previously received ICIs, and followed-up at least once. Logistic regression models examined predictors of aRP (including a priori evaluation of MLD, V20, and V5), and their discriminative capacity was assessed by receiver operating characteristic analysis.

Results

Median follow-up of the 40 patients was 5.3 months. Cancers were lung (80%) or esophageal (20%). ICIs were PD-1 (85%) or PD-L1 (15%) inhibitors (median 4 cycles). Patients underwent definitive (n=19), consolidative (n=14), or palliative (n=7) radiotherapy; the median equivalent dose in 2 Gy fractions (EQD2) was 60 Gy (IQR, 51.8-64 Gy). Grades 1-5 aRP occurred in 25%, 17.5%, 15%, 2.5%, and 5%, respectively. The only variables associated with any-grade aRP were V20 (p=0.014) and MLD (p=0.026), and only V20 with grade ≥2 aRP (p=0.035). Neither the number of prior ICI cycles nor the delivery of concurrent systemic therapy significantly associated with aRP risk. Graphs were constructed showing the incrementally increasing risk of aRP based on V20 and MLD (continuous variables).

Conclusions

This is the first study illustrating that V20 and MLD may impact aRP in the setting of prior ICIs. However, these data should not be extrapolated to patients without pre-radiotherapy receipt of prior ICIs, or to evaluate the risk of chronic pulmonary effects. If these results are validated by larger studies with more homogeneous populations, the commonly accepted V20/MLD dose constraints could require revision if utilized in the setting of ICIs.

Dosimetry impact of gating latency in cine magnetic resonance image guided breath-hold pancreatic cancer radiotherapy

Objective.We investigated dosimetry effect of gating latency in cine magnetic resonance image (cine MRI) guided breath-hold pancreatic cancer radiotherapy.Approach.The gating latency was calculated based on cine MRI obtained from 17 patients who received MRI guided radiotherapy. Because of the cine MRI-related latency, beam overshoot occurs when beam remains on while the tracking target already moves out of the target boundary. The number of beam on/off events was calculated from the cine MRI data. We generated both IMRT and VMAT plans for all 17 patients using 33 Gy prescription, and created motion plans by applying isocenter shift that corresponds to motion-induced tumor displacement. The GTV and PTV coverage and dose to nearby critical structures were compared between the motion and original plan to evaluate the dosimetry change caused by cine MRI latency.Main results.The time ratio of cine MRI imaging latency over the treatment duration is 6.6 ± 3.1%, the mean and median percentage of beam-on events <4 s are 67.0 ± 14.3% and 66.6%. When a gating boundary of 4 mm and a target-out threshold of 5% is used, there is no significant difference for GTV V33Gy between the motion and original plan (p = 0.861 and 0.397 for IMRT and VMAT planning techniques, respectively). However, the PTV V33Gy and stomach Dmax for the motion plans are significantly lower; duodenum V12.5 Gy and V18Gy are significantly higher when compared with the original plans, for both IMRT and VMAT planning techniques.Significance.The cine MRI gating latency can significantly decrease the dose delivered to the PTV, and increase the dose to the nearby critical structures. However, no significant difference is observed for the GTV coverage. The dosimetry impact can be mitigated by implementing additional beam-on control techniques which reduces unnecessary beam on events and/or by using faster cine MRI sequences which reduces the latency period.

Three-dimensional deep neural network for automatic delineation of cervical cancer in planning computed tomography images

Purpose

Radiation therapy is an essential treatment modality for cervical cancer, while accurate and efficient segmentation methods are needed to improve the workflow. In this study, a three‐dimensional V‐net model is proposed to automatically segment clinical target volume (CTV) and organs at risk (OARs), and to provide prospective guidance for low lose area.

Material and methods

A total of 130 CT datasets were included. Ninety cases were randomly selected as the training data, with 10 cases used as the validation data, and the remaining 30 cases as testing data. The V‐net model was implemented with Tensorflow package to segment the CTV and OARs, as well as regions of 5 Gy, 10 Gy, 15 Gy, and 20 Gy isodose lines covered. The auto‐segmentation by V‐net was compared to auto‐segmentation by U‐net. Four representative parameters were calculated to evaluate the accuracy of the delineation, including Dice similarity coefficients (DSC), Jaccard index (JI), average surface distance (ASD), and Hausdorff distance (HD).

Results

The V‐net and U‐net achieved the average DSC value for CTV of 0.85 and 0.83, average JI values of 0.77 and 0.75, average ASD values of 2.58 and 2.26, average HD of 11.2 and 10.08, respectively. As for the OARs, the performance of the V‐net model in the colon was significantly better than the U‐net model (p = 0.046), and the performance in the kidney, bladder, femoral head, and pelvic bones were comparable to the U‐net model. For prediction of low‐dose areas, the average DSC of the patients’ 5 Gy dose area in the test set were 0.88 and 0.83, for V‐net and U‐net, respectively.

Conclusions

It is feasible to use the V‐Net model to automatically segment cervical cancer CTV and OARs to achieve a more efficient radiotherapy workflow. In the delineation of most target areas and OARs, the performance of V‐net is better than U‐net. It also offers advantages with its feature of predicting the low‐dose area prospectively before radiation therapy (RT).

A195 GEOEPIDEMIOLOGICAL CHANGES DUE TO ALTERATIONS IN DIAGNOSTIC ROME CRITERIA FOR IRRITABLE BOWEL SYNDROME

Background

Irritable bowel syndrome (IBS) is a positive diagnosis based on consensus opinions. The Rome (R) criteria, have gone through 4 rounds. R4 restricted diagnosis of IBS over R3, reducing global prevalence. Previously, there were no correlations of world distributions of IBS and Inflammatory Bowel Disease [IBD](Lovell). There are also epidemiological studies showing relationships between IBD and national wealth.

Aims

We reevaluate relationships among different R criteria and latitude(Lat), lactose digestion status (LNP), human development index(HDI), gross domestic product/capita(GDP/c), Crohn’s disease (CD and ulcerative colitis (UC) incidence(i) and prevalence(p).

Methods

Literature on world prevalence of national IBS (Oka,Sperber), IBD rates (Ng), and GDP (Lopez Ruiz) (4) were sought on PubMed and Google Scholar.National HDI(08 or 2016) were available on the internet (http://hdr.undp.org/en/data). National lactase distributions and latitudes were quoted (Szilagyi). Target dates were for the year 2008±8yrs. After log transformation of IBD incidence, Pearson’s correlations were carried out.(strong at; r ≥0.7, moderate ≥0.5, weak ≤ 0.49, negligible ≤ 0.3). (significance was p&lt;0.05).

Results

Correlations of HDI08 and HDI16 was 0.98. Comparisons of R2 and a composite based on Manning, R 1 and 2 (Lovell) was r = 0.96 (N21 countries), Correlation of R 3 and 4 were r = 0.85 (N17). Correlations of R2 and R3 or R4 were negligible (r = -0.2 (n15) and -0.12 (N22). Correlations of R2 and both economic metrics were weak but significant (r = 0.42 – r = 0.49, p &lt; 0.03 - &lt; 0.001). However, correlations of metrics with R3 were non significant and negligible with R4. Comparisons of R2 with LNP or Lat were negligible, but those of R3 or R4 with Lat were significant. Comparisons of R3 with CDp showed a strong correlation while R4 showed a weak but significant correlation with UCi. R2 had negligible correlations with IBD.

Conclusions

Although, these results are based on limited data variations in R criteria have changed relations with IBS prevalence.Earlier R criteria showed increased IBS in poorer nations. The recent R3 and R4 criteria are independent of national economy, but show more relations with increasing latitudes. Consequences of the change include R3 and 4 reflecting western society symptoms perhaps linking it more with IBD.It is unclear if this new relationship incorporates an irritable inflammatory bowel syndrome (Gajula).The generalizability therefore of the new R4 criteria may still be limited.

Table: Rome criteria compared to variables.

Statistical significance * &lt; 0.03, ** ≤ 0.05

Funding Agencies

None

Cone Beam CT (CBCT) Based Synthetic CT Generation Using Deep Learning Methods for Dose Calculation of Nasopharyngeal Carcinoma Radiotherapy

Objective: To generate synthetic CT (sCT) images with high quality from CBCT and planning CT (pCT) for dose calculation by using deep learning methods. Methods: 169 NPC patients with a total of 20926 slices of CBCT and pCT images were included. In this study the CycleGAN, Pix2pix and U-Net models were used to generate the sCT images. The Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Peak Signal to Noise Ratio (PSNR), and Structural Similarity Index (SSIM) were used to quantify the accuracy of the proposed models in a testing cohort of 34 patients. Radiation dose were calculated on pCT and sCT following the same protocol. Dose distributions were evaluated for 4 patients by comparing the dose-volume-histogram (DVH) and 2D gamma index analysis. Results: The average MAE and RMSE values between sCT by three models and pCT reduced by 15.4 HU and 26.8 HU at least, while the mean PSNR and SSIM metrics between sCT by different models and pCT added by 10.6 and 0.05 at most, respectively. There were only slight differences for DVH of selected contours between different plans. The passing rates of 2D gamma index analysis under 3 mm/3% 3 mm/2%, 2 mm/3%and 2 mm/2% criteria were all higher than 95%. Conclusions: All the sCT had achieved better evaluation metrics than those of original CBCT, while the performance of CycleGAN model was proved to be best among three methods. The dosimetric agreement confirmed the HU accuracy and consistent anatomical structures of sCT by deep learning methods.

Adaptive Attention Convolutional Neural Network for Liver Tumor Segmentation

Purpose

Accurate segmentation of liver and liver tumors is critical for radiotherapy. Liver tumor segmentation, however, remains a difficult and relevant problem in the field of medical image processing because of the various factors like complex and variable location, size, and shape of liver tumors, low contrast between tumors and normal tissues, and blurred or difficult-to-define lesion boundaries. In this paper, we proposed a neural network (S-Net) that can incorporate attention mechanisms to end-to-end segmentation of liver tumors from CT images.

Methods

First, this study adopted a classical coding-decoding structure to realize end-to-end segmentation. Next, we introduced an attention mechanism between the contraction path and the expansion path so that the network could encode a longer range of semantic information in the local features and find the corresponding relationship between different channels. Then, we introduced long-hop connections between the layers of the contraction path and the expansion path, so that the semantic information extracted in both paths could be fused. Finally, the application of closed operation was used to dissipate the narrow interruptions and long, thin divide. This eliminated small cavities and produced a noise reduction effect.

Results

In this paper, we used the MICCAI 2017 liver tumor segmentation (LiTS) challenge dataset, 3DIRCADb dataset and doctors' manual contours of Hubei Cancer Hospital dataset to test the network architecture. We calculated the Dice Global (DG) score, Dice per Case (DC) score, volumetric overlap error (VOE), average symmetric surface distance (ASSD), and root mean square error (RMSE) to evaluate the accuracy of the architecture for liver tumor segmentation. The segmentation DG for tumor was found to be 0.7555, DC was 0.613, VOE was 0.413, ASSD was 1.186 and RMSE was 1.804. For a small tumor, DG was 0.3246 and DC was 0.3082. For a large tumor, DG was 0.7819 and DC was 0.7632.

Conclusion

S-Net obtained more semantic information with the introduction of an attention mechanism and long jump connection. Experimental results showed that this method effectively improved the effect of tumor recognition in CT images and could be applied to assist doctors in clinical treatment.

The associations of CD4 count, CD4/CD8 ratio, and HIV viral load with survival from non-small cell lung cancer in persons living with HIV

HIV status may influence survival from non-small cell lung cancer (NSCLC). Among NSCLC patients in the Bronx, NY, we assessed (1) associations of CD4 count, CD4/CD8 ratio and HIV viral load (VL) with survival and (2) prognostic factors among persons living with HIV (PLWH). We compared survival from NSCLC diagnosis (2004-2017) between HIV-negative persons (HIV-, n=2,881) and PLWH (n=88) accounting for clinical and sociodemographic factors. HIV-survival was also compared with PLWH, dichotomized by CD4 (<200 vs. ≥200cells/µL), CD4/CD8 (median, <0.43 vs. ≥0.43) and VL (<75 vs. ≥75copies/mL) at NSCLC diagnosis. Among PLWH, we assessed the relationships of CD4, CD4/CD8, and VL with survival, adjusting for age, sex, and cancer stage. PLWH with CD4< 200cells/µL had lower survival than HIV- [hazard ratio, 95% confidence interval [HR(95%CI)]=1.86(0.98-3.55)]. Survival was similar between PLWH with CD4≥ 200cells/µL and HIV- [HR(95%CI) = 0.90(0.61-1.33)]. Results were similar when categorizing PLWH by CD4/CD8 [vs. HIV-: low CD4/CD8: HR(95%CI) = 1.74(1.07-3.89); high CD4/CD8: HR(95%CI) = 0.63(0.37-1.07)] and VL [vs. HIV-: <75copies/mL: HR(95%CI) = 0.74(0.46-1.21), ≥75copies/mL: HR(95%CI) = 1.41(0.88-2.27)]. Among PLWH, CD4< 200cells/µL was associated with worse survival [vs. CD4≥ 200cells/µL: HR(95%CI) = 2.37(1.14-4.92)]. CD4, CD4/CD8, and VL may be prognostic markers for PLWH with NSCLC, suggesting immune status may be important in NSCLC survival among PLWH.

Association of waist circumstance with long-term all-cause mortality and cardiac death in patients with a pacemaker

Funding Acknowledgements

Type of funding sources: None.

Objective

To explore the association of abdominal obesity with long-term prognosis in patients with a pacemaker.

Methods

Patients in the Summit study were enrolled and divided into groups according to baseline waist circumference: with obesity, normal, and lean. Regular follow-up was performed. The primary endpoint was all-cause mortality, and the secondary endpoint was cardiac death.

Results

In total, 492 patients were included in the analysis. The average baseline waist circumference was 84.2 ± 12.7 cm, and abdominal obesity was observed in 37.6% of patients. During a mean follow-up of 67.2 ± 17.5 months,71 all-cause mortality (14.40%) and 24 cardiac death (4.87%) events occurred. All-cause mortality was associated with higher waist circumference (87.6 versus 83.6 cm, P = 0.014), but not body mass index (23.6 versus 23.5, P= 0.930). Multivariate Cox analysis showed compared with patients with abdominal obesity, lean patients had a significant survival benefit in both all-cause mortality (HR 0.188, 95%CI 0.070-0.505, P = 0.001) and cardiac death (HR 0.097, 95% CI 0.012-0.792, P = 0.029).

Conclusions

Waist circumference was associated with long-term all-cause mortality and cardiac death. Baseline waist circumference less than 80 cm for men and less than 75 cm for women had a significant survival benefit.

A triple-classification radiomics model for the differentiation of pleomorphic adenoma, Warthin tumour, and malignant salivary gland tumours on the basis of diffusion-weighted imaging

AIM

To develop and validate a triple-classification radiomics model for the preoperative differentiation of pleomorphic adenoma (PA), Warthin tumour (WT), and malignant salivary gland tumour (MSGT) based on diffusion-weighted imaging (DWI).

MATERIALS AND METHODS

Data from 217 patients with histopathologically confirmed salivary gland tumours (100 PAs, 68 WTs, and 49 MSGTs) from January 2015 to March 2019 were analysed retrospectively and divided into a training set (n=173), and a validation set (n=44). A total of 396 radiomic features were extracted from the DWI of all patients. Analysis of variance (ANOVA) and least absolute shrinkage and selection operator (LASSO) regression were used to select radiomic features, which were then constructed using three classification models, namely, logistic regression method (LR), support vector machine (SVM), and K-nearest neighbor (KNN). The diagnostic performance of the radiomics model was quantified by the receiver operating characteristic (ROC) curve and area under the ROC curve (AUC) of the training and validation data sets.

RESULTS

The 20 most valuable features were investigated based on the LASSO regression. LR and SVM methods exhibited better diagnostic ability than KNN for multiclass classification. LR and SVM had the best performance and yielded the AUC values of 0.857 and 0.824, respectively, in the training data set and the AUC values of 0.932 and 0.912, respectively, in the validation data set of MSGT diagnosis.

CONCLUSION

DWI-based triple-classification radiomics model has predictive value in distinguishing PA, WT, and MSGT, which can be used for preoperative auxiliary diagnosis in clinical practice.

Synthesis of silver nanoparticles using Lactobacillus bulgaricus and assessment of their antibacterial potential

Many pathogenic strains have acquired multidrug-resistant patterns in recent a year, which poses a major public health concern. The growing need for effective antimicrobial agents as novel therapies against multidrug-resistant pathogens has drawn scientist attention toward nanotechnology. Silver nanoparticles are considered capable of killing multidrug-resistant isolates due to their oligo-dynamic effect on microorganisms. In this research study NPs were synthesized using the gram-positive bacteria Lactobacillus bulgaricus and its activity against selected pathogenic strains. Lactobacillus bulgaricus pure cultures were isolated from raw milk and grown in "De Man, Rogasa, and Sharp" broth for synthesis of nanoparticles. Lactobacillus bulgaricus culture was centrifuged and Cell- free supernatant of it was employed with aqueous silvery ions and evaluated their antibacterial activities against bacterial strains i.e. Staphylococcus aureus, Staphylococcus epidermidis and Salmonella typhi using agar well diffusion assay. Antibiotic profiling against selected pathogenic strains were also conducted using disc diffusion method. The synthesis and characterization of silver nanoparticles were monitored primarily by the conversion of the pale-yellow color of the mixture into a dark-brown color and via ultraviolet-visible absorption spectroscopy and Scanning electron microscopy respectively. The result showed that that AgNPs with size (30.65-100 nm) obtained from Lactobacillus bulgaricus were found to exhibit antibacterial activities against selected bacterial strains. Taken together, these findings suggest that Lactobacillus bulgaricus has great potential for the production of AgNPs with antibacterial activities and highly effective in comparison to tested antibiotics.

In situ growth of large-area and self-aligned graphene nanoribbon arrays on liquid metal

Intrinsic graphene features semi-metallic characteristics that limit its applications in electronic devices, whereas graphene nanoribbons (GNRs) are promising semiconductors because of their bandgap-opening feature. However, the controllable mass-fabrication of high-quality GNR arrays remains a major challenge. In particular, the in situ growth of GNR arrays through template-free chemical vapor deposition (CVD) has not been realized. Herein, we report a template-free CVD strategy to grow large-area, high-quality and self-aligned GNR arrays on liquid copper surface. The width of as-grown GNR could be optimized to sub-10 nm with aspect ratio up to 387, which is higher than those of reported CVD-GNRs. The study of the growth mechanism indicates that a unique comb-like etching-regulated growth process caused by a trace hydrogen flow guides the formation of the mass-produced self-aligned GNR arrays. Our approach is operationally simple and efficient, offering an assurance for the use of GNR arrays in integrated circuits.

[Study on syndromic deafness caused by novel pattern of compound heterozygous variants in the CDH23 gene]

Objective: To explore the pathogenic variants of a family with syndromic deafness by high-throughput sequencing. Methods: The family was from Puyang City, Henan Province, and had four members, including two with syndromic deafness. The proband and his sister had congenital deafness, and their parents had normal phenotypes. The clinical phenotype of the family was characterized using clinical examinations and pedigree analysis. The clinical examinations included imaging examination, audiometry (pure tone audiometry, acoustic immittance, brainstem auditory evoked potential, and otoacoustic emission), vestibular function test, and ophthalmic examination (visual acuity test, visual field test, fundus examination, visual evoked potential, and electroretinogram). Target exome sequencing of 129 known deafness genes and bioinformatics analysis were used to screen suspected pathogenic variants. Sanger sequencing and minigene assay were used to verify and functionally investigate the mutation detected, respectively. According to the standards and guidelines for interpreting genetic variants proposed by the American College of Medical Genetics and Genomics, the variants c.6049G>A and c.8699A>G were classified as pathogenic/likely pathogenic, and the variant c.9856C>G was classified as variants of uncertain significance. Results: The probands and his sister had severe sensorineural hearing loss with decreased binocular vision, night blindness, decreased peripheral visual field sensitivity and partial visual field defect, and normal vestibular function. Both of them had three CDH23 mutations, including CDH23 (NM_022124.5) c.6049G>A (p.Gly2017Ser),c.9856C>G (p.His3286Asp), and c.8699A>G (p. Asp2900Gly), The first two were inherited from the father, and the last one was from the mother. The missense variants c.9856C>G and c.8699A>G were not included in the gnomad database. The missense mutation c.6049G>A was located in the last position of exon 46 and was predicted to affect splicing by bioinformatics software. The minigene experiment showed that the mutation cause exon skipping of exon 46, resulting in an abnormal protein. Conclusions: Compound heterozygous variations of the CDH23 are the leading cause of USH1D in the family. This study confirms that the compound heterozygosity of splicing and missense variants of the CDH23 gene could lead to USH1D.

Effect of condylar osteochondroma resection through an intraoral approach on the masticatory functions: a preliminary evaluation based on occlusion and temporomandibular joint functions

With this research, we aimed to evaluate the effect of condylar osteochondroma (OC) resection through an intraoral approach on the masticatory functions. Resection of condylar OC was carried out via an intraoral approach with the help of three-dimensional (3D) design, endoscope, and navigation system. The T-Scan III computerised occlusal analysis system was used to evaluate the occlusal force distribution, recorded at pre-treatment (T1) and post-treatment (T2) intervals. Records of the clinical examination of the temporomandibular joint (TMJ), including maximal interincisal opening, mandibular lateral and forward movements, were also collected. Ten patients with condylar OC were enrolled in this study. The difference of force distribution between bilateral occlusion was reduced in T2 compared with T1 (11.92% ± 4.41% vs 48.52 % ± 28.37%, p<0.05), indicating better occlusal force distribution obtained after surgery. There was no significant difference in functions of the TMJ, such as maximal interincisal opening, and mandibular lateral and forward movements between T2 and T1 (p>0.05). Accordingly, condylar OC resection through an intraoral approach would obtain a satisfactory occlusal balance with no impairment of the temporomandibular joint functions.

Sequential and Iterative Auto-Segmentation of High-Risk Clinical Target Volume for Radiotherapy of Nasopharyngeal Carcinoma in Planning CT Images

Background: Accurate segmentation of tumor targets is critical for maximizing tumor control and minimizing normal tissue toxicity. We proposed a sequential and iterative U-Net (SI-Net) deep learning method to auto-segment the high-risk primary tumor clinical target volume (CTVp1) for treatment planning of nasopharyngeal carcinoma (NPC) radiotherapy. Methods: The SI-Net is a variant of the U-Net architecture. The input of SI-Net includes one CT image, the CTVp1 contour on this image, and the next CT image. The output is the predicted CTVp1 contour on the next CT image. We designed the SI-Net, using the left side to learn the volumetric features and the right to localize the contour on the next image. Two prediction directions, one from inferior to superior (forward direction) and the other from superior to inferior (backward direction), were tested. The performance was compared between the SI-Net and the U-Net using Dice similarity coefficient (DSC), Jaccard index (JI), average surface distance (ASD), and Hausdorff distance (HD) metrics. Results: The DSC and JI values from the forward direction SI-Net model were 5 and 6% higher than those from the U-Net model (0.84 ± 0.04 vs. 0.80 ± 0.05 and 0.74 ± 0.05 vs. 0.69 ± 0.05, p < 0.001). The smaller ASD and HD values also indicated a better performance (2.8 ± 1.0 vs. 3.3 ± 1.0 mm and 8.7 ± 2.5 vs. 9.7 ± 2.7 mm, p < 0.01) for the SI-Net model. For the backward direction SI-Net model, the DSC and JI values were still better than those from the U-Net model (p < 0.01), although there were no significant differences in ASD and HD. Conclusions: The SI-Net model preserved the continuity between adjacent images and thus improved the segmentation accuracy compared with the conventional U-Net model. This model has potential of improving the efficiency and consistence of CTVp1 contouring for NPC patients.

Dorsal-ventral patterned neural cyst from human pluripotent stem cells in a neurogenic niche

Despite its importance in central nervous system development, development of the human neural tube (NT) remains poorly understood, given the challenges of studying human embryos, and the developmental divergence between humans and animal models. We report a human NT development model, in which NT-like tissues, neuroepithelial (NE) cysts, are generated in a bioengineered neurogenic environment through self-organization of human pluripotent stem cells (hPSCs). NE cysts correspond to the neural plate in the dorsal ectoderm and have a default dorsal identity. Dorsal-ventral (DV) patterning of NE cysts is achieved using retinoic acid and/or sonic hedgehog and features sequential emergence of the ventral floor plate, P3, and pMN domains in discrete, adjacent regions and a dorsal territory progressively restricted to the opposite dorsal pole. This hPSC-based, DV patterned NE cyst system will be useful for understanding the self-organizing principles that guide NT patterning and for investigations of neural development and neural disease.