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BackgroundRadiomics can improve the accuracy of traditional image diagnosis to evaluate extrahepatic cholangiocarcinoma (ECC); however, this is limited by variations across radiologists, subjective evaluation, and restricted data. A radiomics-based particle swarm optimization and support vector machine (PSO-SVM) model may provide a more accurate auxiliary diagnosis for assessing differentiation degree (DD) and lymph node metastasis (LNM) of ECC. ObjectiveThe objective of our study is to develop a PSO-SVM radiomics model for predicting DD and LNM of ECC. MethodsFor this retrospective study, the magnetic resonance imaging (MRI) data of 110 patients with ECC who were diagnosed from January 2011 to October 2019 were used to construct a radiomics prediction model. Radiomics features were extracted from T1-precontrast weighted imaging (T1WI), T2-weighted imaging (T2WI), and diffusion-weighted imaging (DWI) using MaZda software (version 4.6; Institute of Electronics, Technical University of Lodz). We performed dimension reduction to obtain 30 optimal features of each sequence, respectively. A PSO-SVM radiomics model was developed to predict DD and LNM of ECC by incorporating radiomics features and apparent diffusion coefficient (ADC) values. We randomly divided the 110 cases into a training group (88/110, 80%) and a testing group (22/110, 20%). The performance of the model was evaluated by analyzing the area under the receiver operating characteristic curve (AUC). ResultsA radiomics model based on PSO-SVM was developed by using 110 patients with ECC. This model produced average AUCs of 0.8905 and 0.8461, respectively, for DD in the training and testing groups of patients with ECC. The average AUCs of the LNM in the training and testing groups of patients with ECC were 0.9036 and 0.8889, respectively. For the 110 patients, this model has high predictive performance. The average accuracy values of the training group and testing group for DD of ECC were 82.6% and 80.9%, respectively; the average accuracy values of the training group and testing group for LNM of ECC were 83.6% and 81.2%, respectively. ConclusionsThe MRI-based PSO-SVM radiomics model might be useful for auxiliary clinical diagnosis and decision-making, which has a good potential for clinical application for DD and LNM of ECC.

Lensless imaging represents a significant advancement in imaging technology, offering unique benefits over traditional optical systems due to its compact form factor, ideal for applications within the Internet of Things (IoT) ecosystem. Despite its potential, the intensive computational requirements of current lensless imaging reconstruction algorithms pose a challenge, often exceeding the resource constraints typical of IoT devices. To meet this challenge, a novel approach is introduced, merging multi-level image restoration with the pix2pix generative adversarial network architecture within the lensless imaging sphere. Building on the foundation provided by U-Net, a Multi-level Attention-based Lensless Image Restoration Network (MARN) is introduced to further augment the generator's capabilities. In this methodology, images reconstructed through Tikhonov regularization are perceived as degraded images, forming the foundation for further refinement via the Pix2pix network. This process is enhanced by incorporating an attention-focused mechanism in the encoder--decoder structure and by implementing stage-wise supervised training within the deep convolutional network, contributing markedly to the improvement of the final image quality. Through detailed comparative evaluations, the superiority of the introduced method is affirmed, outperforming existing techniques and underscoring its suitability for addressing the computational challenges in lensless imaging within IoT environments. This method can produce excellent lensless image reconstructions when sufficient computational resources are available, and it consistently delivers optimal results across varying computational resource constraints. This algorithm enhances the applicability of lensless imaging in applications such as the Internet of Things, providing higher-quality image acquisition and processing capabilities for these domains. [ABSTRACT FROM AUTHOR]

In solar applications, the solar absorber is paramount to converting solar radiation to heat energy. We systematically examined the relationship between the efficiency of the solar absorber and operating temperature and other factors. By combining inverse designs with surface plasmonic and Fabry-Perot cavity solar absorption theories, we have developed several solar absorber devices with excellent performance at different temperatures. One of these devices displays a solar spectral absorption of 95.6%, an ultra-low emission rate of 5.7%, and optical-to-thermal conversion efficiency exceeding 90%, all within an ultra-thin depth of 0.45 μm under working temperatures of 600 K. The device has the potential to surpass the Shockley-Queisser limit (S-Q limit) in solar power generation systems. Our method is adaptable, enabling the design of optimal-performance devices to the greatest extent possible. The design was optimized using modern optimization algorithms to meet complex conditions and offers new insights for further study of the conversion from solar to thermal energy and the advancement of solar energy applications. [ABSTRACT FROM AUTHOR]

Due to the imbalance of multiple special-shaped cavities in the family mould, there are some defects such as short shot and delaying effect easily caused during the injection molding process. This results in a poor quality of the formed products. Based on the melt flow analysis, the section radius and length of the runner were determined as two important factors affecting the volume flow and pressure distribution of the melt. Considering of the complex mold structures for indication lamp post, the maximum difference of filling pressures at the end of filling process among different sub-runner was taken as an unbalance factor, and an integrated rapid optimum mechanism with a multivariate structure variety was constructed according to the uniform design method. The optimal runner scheme was obtained by combining with the global optimization of the genetic algorithm. The numerical simulation results indicated that the runner system with improved structure parameters has a better balance filling effect than that with the initial ones. [ABSTRACT FROM AUTHOR]

Preoperative identification of axillary lymph node metastasis can play an important role in treatment selection strategy and prognosis evaluation. This study aimed to establish a clinical nomogram based on lymph node images to predict lymph node metastasis in breast cancer patients. A total of 193 patients with non-specific invasive breast cancer were divided into training (n = 135) and validation set (n = 58). Radiomics features were extracted from lymph node images instead of tumor region, and the least absolute shrinkage and selection operator logistic algorithm was used to select the extracted features and generate radiomics score. Then, the important clinical factors and radiomics score were integrated into a nomogram. A receiver operating characteristic curve was used to evaluate the nomogram, and the clinical benefit of using the nomogram was evaluated by decision curve analysis. We found that clinical N stage and radiomics score were independent clinical predictors. Besides, the nomogram accurately predicted axillary lymph node metastasis, yielding an area under the receiver operating characteristic curve of 0.95 (95% confidence interval 0.93-0.98) in the validation set, indicating satisfactory calibration. Decision curve analysis confirmed that the nomogram had higher clinical utility than clinical N stage or radiomics score alone. Overall, the nomogram based on radiomics features and clinical factors can help radiologists to predict axillary lymph node metastasis preoperatively and provide valuable information for individual treatment.

Objective: To summarize the clinical features, diagnosis and treatment experience of IgG4-related lung disease for the sake of improving clinical understanding of this disease and reducing the misdiagnosis and mistreatment rates., Methods: To analyze the general situation, clinical manifestation, laboratory examination, histopathology, therapy and prognosis of 2 patients with IgG4-related lung disease, who were admitted in the department of respiratory diseases at Changhai Hospital. Publications related to IgG4 lung disease were reviewed., Results: Both patients were male with elevated serum IgG4 level (2.25 g/L and 10 g/L respectively). In one patient, radiologic finding showed solid nodules in the lung with ileocecal involvement. He responded well to glucocorticoid. The other patient's computed tomography of lung demonstrated bronchovascular type. Glucocorticoid therapy was effective to both patients. A total of 69 cases with IgG4-related lung disease were reported worldwide, among whom 39 cases were admitted with chief complaints of respiratory symptoms. However, there were 41 cases suffering extra-pulmonary involvement. Serum IgG4 levels detected in 48 cases were significantly elevated (307-52 500 mg/L). The radiographic pattern of solid nodule type was the most frequent, accounting for 50.7% (35 cases). A total of 31 (44.9%) patients received glucocorticoid therapy with good response and prognosis., Conclusion: IgG4-related lung disease is a rare immunological disease lack of specific symptoms. Serum immunological examination, radiographic characteristics and histopathology should be comprehensively considered for diagnosis. Glucocorticoid is so far the most acceptable therapy with good response rate.