Spiral grass inspired eco-friendly zein fibrous membrane for multi-efficient air purification.

Air pollution, one of the severest threats to public health, may lead to cardiovascular and respiratory illnesses. In order to cope with the deteriorating air pollutant, there is an increasing demand for filters with high purification efficiency, but it's tough to strike a balance between efficiency and resistance. Fabricating an eco-friendly fibrous filter which can capture both PM2.5 and gaseous chemical hazards with high efficiency but under ultra-low resistance is a long-term challenge. Herein, inspired by the interesting ribbon shape of spiral grass, a green and robust 3D nonwoven membrane with controllable hierarchical structure made of self-curved zein nanofibers modified by zeolitic imidazolate framework-8 (ZIF-8) via bi-solvent electrospinning and fumigation welding method was fabricated. The obtained ZIF-8 modified zein membranes showed extraordinary overall performance with high PM2.5 removal efficiency (99.04 %) at a low stress drop (54.87 Pa), first-rate formaldehyde removal efficiency (98.8 %) and excellent photocatalytic antibacterial. In addition, the relatively weak mechanical properties of zein fibrous membranes have been improved via solvent fumigation welding of the joint zein fibers. This study provides a green and convenient insight to the manufacturing of environmentally-friendly zein fibrous membranes with high filtration efficiency, low air resistance and high formaldehyde removal for sustainable air remediation.

Artificial intelligence applications in brachytherapy: A literature review.

PURPOSE: Artificial intelligence (AI) has the potential to simplify and optimize various steps of the brachytherapy workflow, and this literature review aims to provide an overview of the work done in this field. METHODS AND MATERIALS: We conducted a literature search in June 2022 on PubMed, Embase, and Cochrane for papers that proposed AI applications in brachytherapy. RESULTS: A total of 80 papers satisfied inclusion/exclusion criteria. These papers were categorized as follows: segmentation (24), registration and image processing (6), preplanning (13), dose prediction and treatment planning (11), applicator/catheter/needle reconstruction (16), and quality assurance (10). AI techniques ranged from classical models such as support vector machines and decision tree-based learning to newer techniques such as U-Net and deep reinforcement learning, and were applied to facilitate small steps of a process (e.g., optimizing applicator selection) or even automate the entire step of the workflow (e.g., end-to-end preplanning). Many of these algorithms demonstrated human-level performance and offer significant improvements in speed. CONCLUSIONS: AI has potential to augment, automate, and/or accelerate many steps of the brachytherapy workflow. We recommend that future studies adhere to standard reporting guidelines. We also stress the importance of using larger sample sizes and reporting results using clinically interpretable measures.

Edible pullulan enhanced water-soluble keratin with improved sizing performance for sustainable textile industry.

Feather keratin from waste feather has become an attractive target to replace petroleum-based Poly (vinyl alcohol) sizes due to its easy film-forming ability, excellent adhesive property, biodegradability and low cost. However, poor water-solubility and brittleness of pure keratin films have become the bottlenecks and restricted the application of keratin as sizing agents. Therefore, water-soluble keratin was extracted by the reduction-preservation method and enhanced by saccharides in aqueous system to obtain all-green keratin-based slurry. The results showed that the keratin-based slurry exhibited improved sizing performance in the order of sucrose ≤ glucose ≤ pullulan by the moderate Maillard reaction. Among them, the fabricated pullulan-keratin sizes films had 27.86 %, 2684.08 % and 2911.31 % increment in tensile strength, elongation and work of facture compared with pure keratin sizes films. Besides, the addition of pullulan and subsequently moderate Maillard reaction improved the thermo-tenacity of keratin-based sizes, which was expected to tackle with the brittleness of pure keratin size films. In addition, novel pullulan-keratin sizes had good sizing performance and high desizing efficiency to cotton, cotton/polyester and polyester yarns and fabrics. Successful utilization of pullulan-keratin sizes will bring opportunities for high value utilization of waste feather and promote the green and low-carbon development of textile industry.

Deep learning attention-guided radiomics for COVID-19 chest radiograph classification.

Background: Accurate assessment of coronavirus disease 2019 (COVID-19) lung involvement through chest radiograph plays an important role in effective management of the infection. This study aims to develop a two-step feature merging method to integrate image features from deep learning and radiomics to differentiate COVID-19, non-COVID-19 pneumonia and normal chest radiographs (CXR). Methods: In this study, a deformable convolutional neural network (deformable CNN) was developed and used as a feature extractor to obtain 1,024-dimensional deep learning latent representation (DLR) features. Then 1,069-dimensional radiomics features were extracted from the region of interest (ROI) guided by deformable CNN's attention. The two feature sets were concatenated to generate a merged feature set for classification. For comparative experiments, the same process has been applied to the DLR-only feature set for verifying the effectiveness of feature concatenation. Results: Using the merged feature set resulted in an overall average accuracy of 91.0% for three-class classification, representing a statistically significant improvement of 0.6% compared to the DLR-only classification. The recall and precision of classification into the COVID-19 class were 0.926 and 0.976, respectively. The feature merging method was shown to significantly improve the classification performance as compared to using only deep learning features, regardless of choice of classifier (P value <0.0001). Three classes' F1-score were 0.892, 0.890, and 0.950 correspondingly (i.e., normal, non-COVID-19 pneumonia, COVID-19). Conclusions: A two-step COVID-19 classification framework integrating information from both DLR and radiomics features (guided by deep learning attention mechanism) has been developed. The proposed feature merging method has been shown to improve the performance of chest radiograph classification as compared to the case of using only deep learning features.

Deep Learning-Based Automatic Assessment of Radiation Dermatitis in Patients With Nasopharyngeal Carcinoma.

PURPOSE: Radiation dermatitis (RD) is a common, unpleasant side effect of patients receiving radiation therapy. In clinical practice, the severity of RD is graded manually through visual inspection, which is labor intensive and often leads to large interrater variations. To overcome these shortcomings, this study aimed to develop an automatic RD assessment based on deep learning (DL) techniques that could efficiently assist the RD severity classification in clinical application. METHODS AND MATERIALS: A total of 1205 photographs of the head and neck region were collected from patients with nasopharyngeal carcinoma (NPC) undergoing radiation therapy. The severity of RD in these photographs was graded by 5 qualified assessors based on the Radiation Therapy Oncology Group guidance. An end-to-end RD grading framework was developed by combining a DL-based segmentation network and a DL-based RD severity classifier, which are used for segmenting the neck region from the camera-captured photographs and grading, respectively. U-Net was used for segmentation and another convolutional neural network classifier (DenseNet-121) was applied to RD severity classification. Dice similarity coefficient was used to evaluate the performance of segmentation. Severity classification was evaluated by several metrics, including overall accuracy, precision, recall, and F1 score. RESULTS: Results of segmentation showed that the averaged dice similarity coefficients were 91.2% and 90.8% for front and side view, respectively. For RD severity classification, the overall accuracy of test photographs was 83.0%. Our method accurately classified 90.5% of grade 0, 67.2% of grade 1, 93.8% of grade 2, and 100% of above grade 2 cases. The overall prediction performance was comparable with human assessors. There was no significant difference in accuracy when using manually or automatically segmented regions (P = .683). CONCLUSIONS: We have successfully demonstrated a DL-based method for automatic assessment of RD severity in patients with NPC. This method holds great potential for efficient and effective assessing and monitoring of RD in patients with NPC.

A Multi-Center Study of CT-Based Neck Nodal Radiomics for Predicting an Adaptive Radiotherapy Trigger of Ill-Fitted Thermoplastic Masks in Patients with Nasopharyngeal Carcinoma.

Significant lymph node shrinkage is common in patients with nasopharyngeal carcinoma (NPC) throughout radiotherapy (RT) treatment, causing ill-fitted thermoplastic masks (IfTMs). To deal with this, an ad hoc adaptive radiotherapy (ART) may be required to ensure accurate and safe radiation delivery and to maintain treatment efficacy. Presently, the entire procedure for evaluating an eligible ART candidate is time-consuming, resource-demanding, and highly inefficient. In the artificial intelligence paradigm, the pre-treatment identification of NPC patients at risk for IfTMs has become greatly demanding for achieving efficient ART eligibility screening, while no relevant studies have been reported. Hence, we aimed to investigate the capability of computed tomography (CT)-based neck nodal radiomics for predicting IfTM-triggered ART events in NPC patients via a multi-center setting. Contrast-enhanced CT and the clinical data of 124 and 58 NPC patients from Queen Elizabeth Hospital (QEH) and Queen Mary Hospital (QMH), respectively, were retrospectively analyzed. Radiomic (R), clinical (C), and combined (RC) models were developed using the ridge algorithm in the QEH cohort and evaluated in the QMH cohort using the median area under the receiver operating characteristics curve (AUC). Delong's test was employed for model comparison. Model performance was further assessed on 1000 replicates in both cohorts separately via bootstrapping. The R model yielded the highest "corrected" AUC of 0.784 (BCa 95%CI: 0.673-0.859) and 0.723 (BCa 95%CI: 0.534-0.859) in the QEH and QMH cohort following bootstrapping, respectively. Delong's test indicated that the R model performed significantly better than the C model in the QMH cohort (p < 0.0001), while demonstrating no significant difference compared to the RC model (p = 0.5773). To conclude, CT-based neck nodal radiomics was capable of predicting IfTM-triggered ART events in NPC patients in this multi-center study, outperforming the traditional clinical model. The findings of this study provide valuable insights for future study into developing an effective screening strategy for ART eligibility in NPC patients in the long run, ultimately alleviating the workload of clinical practitioners, streamlining ART procedural efficiency in clinics, and achieving personalized RT for NPC patients in the future.

A dual-supervised deformation estimation model (DDEM) for constructing ultra-quality 4D-MRI based on a commercial low-quality 4D-MRI for liver cancer radiation therapy.

BACKGROUND: Most available four-dimensional (4D)-magnetic resonance imaging (MRI) techniques are limited by insufficient image quality and long acquisition times or require specially designed sequences or hardware that are not available in the clinic. These limitations have greatly hindered the clinical implementation of 4D-MRI. PURPOSE: This study aims to develop a fast ultra-quality (UQ) 4D-MRI reconstruction method using a commercially available 4D-MRI sequence and dual-supervised deformation estimation model (DDEM). METHODS: Thirty-nine patients receiving radiotherapy for liver tumors were included. Each patient was scanned using a time-resolved imaging with interleaved stochastic trajectories (TWIST)-lumetric interpolated breath-hold examination (VIBE) MRI sequence to acquire 4D-magnetic resonance (MR) images. They also received 3D T1-/T2-weighted MRI scans as prior images, and UQ 4D-MRI at any instant was considered a deformation of them. A DDEM was developed to obtain a 4D deformable vector field (DVF) from 4D-MRI data, and the prior images were deformed using this 4D-DVF to generate UQ 4D-MR images. The registration accuracies of the DDEM, VoxelMorph (normalized cross-correlation [NCC] supervised), VoxelMorph (end-to-end point error [EPE] supervised), and the parametric total variation (pTV) algorithm were compared. Tumor motion on UQ 4D-MRI was evaluated quantitatively using region of interest (ROI) tracking errors, while image quality was evaluated using the contrast-to-noise ratio (CNR), lung-liver edge sharpness, and perceptual blur metric (PBM). RESULTS: The registration accuracy of the DDEM was significantly better than those of VoxelMorph (NCC supervised), VoxelMorph (EPE supervised), and the pTV algorithm (all, p < 0.001), with an inference time of 69.3 ± 5.9 ms. UQ 4D-MRI yielded ROI tracking errors of 0.79 ± 0.65, 0.50 ± 0.55, and 0.51 ± 0.58 mm in the superior-inferior, anterior-posterior, and mid-lateral directions, respectively. From the original 4D-MRI to UQ 4D-MRI, the CNR increased from 7.25 ± 4.89 to 18.86 ± 15.81; the lung-liver edge full-width-at-half-maximum decreased from 8.22 ± 3.17 to 3.65 ± 1.66 mm in the in-plane direction and from 8.79 ± 2.78 to 5.04 ± 1.67 mm in the cross-plane direction, and the PBM decreased from 0.68 ± 0.07 to 0.38 ± 0.01. CONCLUSION: This novel DDEM method successfully generated UQ 4D-MR images based on a commercial 4D-MRI sequence. It shows great promise for improving liver tumor motion management during radiation therapy.

Hemodynamic stress shapes subchondral bone in osteoarthritis: An emerging hypothesis.

Osteoarthritis (OA) is no longer regarded as a simple wear-and-tear problem of articular cartilage. Instead, OA is a whole joint disorder involving both cartilaginous and non-cartilaginous tissues such as subchondral bone and synovium. Among them, subchondral bone undergoes constant remodeling in response to the changes of mechanical environment. Current understanding of subchondral bone disturbance in OA is limited to its link with an altered local mechanical loading as a result of ligament or meniscus injury. Very recently, hypertension, the most common vascular morbidity, has been emerged as an independent risk factor of OA. It might suggest a plausible role of systemic hemodynamic mechanical stress in subchondral bone remodeling and the pathogenesis of OA. However, their relationship remains not fully understood. Based on our preliminary clinical observation on the association of hemodynamic parameters with subchondral bone mass and microstructure in late-stage knee OA patients, we formulate a vascular etiology hypothesis of OA from a mechanobiology perspective. Noteworthily, hemodynamic stress associated with subchondral bone mineral density; yet compressive mechanical loading does not. Furthermore, hemodynamic parameters positively correlated with subchondral plate-like trabecular bone volume but negatively associated with rod-like trabecular bone volume. In contrast, compressive mechanical loading tends to increase both plate-like and rod-like trabecular bone volume. Taken together, it warrants further investigations into the distinct role of hemodynamic or compressive stress in shaping subchondral bone in the pathophysiology of OA. THE TRANSLATIONAL POTENTIAL OF THIS ARTICLE: This work provides a new insight, from the angle of biomechanics, into the emerging role of vascular pathologies, such as hypertension, in the pathogenesis of OA. It might open up a new avenue for the development of a mechanism-based discovery of novel diagnostics and therapeutics.

Multi-Organ Omics-Based Prediction for Adaptive Radiation Therapy Eligibility in Nasopharyngeal Carcinoma Patients Undergoing Concurrent Chemoradiotherapy.

PURPOSE: To investigate the role of different multi-organ omics-based prediction models for pre-treatment prediction of Adaptive Radiotherapy (ART) eligibility in patients with nasopharyngeal carcinoma (NPC). METHODS AND MATERIALS: Pre-treatment contrast-enhanced computed tomographic and magnetic resonance images, radiotherapy dose and contour data of 135 NPC patients treated at Hong Kong Queen Elizabeth Hospital were retrospectively analyzed for extraction of multi-omics features, namely Radiomics (R), Morphology (M), Dosiomics (D), and Contouromics (C), from a total of eight organ structures. During model development, patient cohort was divided into a training set and a hold-out test set in a ratio of 7 to 3 via 20 iterations. Four single-omics models (R, M, D, C) and four multi-omics models (RD, RC, RM, RMDC) were developed on the training data using Ridge and Multi-Kernel Learning (MKL) algorithm, respectively, under 10-fold cross validation, and evaluated on hold-out test data using average area under the receiver-operator-characteristics curve (AUC). The best-performing single-omics model was first determined by comparing the AUC distribution across the 20 iterations among the four single-omics models using two-sided student t-test, which was then retrained using MKL algorithm for a fair comparison with the four multi-omics models. RESULTS: The R model significantly outperformed all other three single-omics models (all p-value<0.0001), achieving an average AUC of 0.942 (95%CI: 0.938-0.946) and 0.918 (95%CI: 0.903-0.933) in training and hold-out test set, respectively. When trained with MKL, the R model (R_MKL) yielded an increased AUC of 0.984 (95%CI: 0.981-0.988) and 0.927 (95%CI: 0.905-0.948) in training and hold-out test set respectively, while demonstrating no significant difference as compared to all studied multi-omics models in the hold-out test sets. Intriguingly, Radiomic features accounted for the majority of the final selected features, ranging from 64% to 94%, in all the studied multi-omics models. CONCLUSIONS: Among all the studied models, the Radiomic model was found to play a dominant role for ART eligibility in NPC patients, and Radiomic features accounted for the largest proportion of features in all the multi-omics models.