A Review of High-Temperature Aerogels: Composition, Mechanisms, and Properties.

High-temperature aerogels have garnered significant attention as promising insulation materials in various industries such as aerospace, automotive manufacturing, and beyond, owing to their remarkable thermal insulation properties coupled with low density. With advancements in manufacturing techniques, the thermal resilience of aerogels has considerable improvements. Notably, polyimide-based aerogels can endure temperatures up to 1000 °C, zirconia-based aerogels up to 1300 °C, silica-based aerogels up to 1500 °C, alumina-based aerogels up to 1800 °C, and carbon-based aerogels can withstand up to 2500 °C. This paper systematically discusses recent advancements in the thermal insulation performance of these five materials. It elaborates on the temperature resistance of aerogels and elucidates their thermal insulation mechanisms. Furthermore, it examines the impact of doping elements on the thermal conductivity of aerogels and consolidates various preparation methods aimed at producing aerogels capable of withstanding temperatures. In conclusion, by employing judicious composition design strategies, it is anticipated that the maximum tolerance temperature of aerogels can surpass 2500 °C, thus opening up new avenues for their application in extreme thermal environments.

Early Predicting Osteogenic Differentiation of Mesenchymal Stem Cells Based on Deep Learning Within One Day.

Osteogenic differentiation of mesenchymal stem cells (MSCs) is proposed to be critical for bone tissue engineering and regenerative medicine. However, the current approach for evaluating osteogenic differentiation mainly involves immunohistochemical staining of specific markers which often can be detected at day 5-7 of osteogenic inducing. Deep learning (DL) is a significant technology for realizing artificial intelligence (AI). Computer vision, a branch of AI, has been proved to achieve high-precision image recognition using convolutional neural networks (CNNs). Our goal was to train CNNs to quantitatively measure the osteogenic differentiation of MSCs. To this end, bright-field images of MSCs during early osteogenic differentiation (day 0, 1, 3, 5, and 7) were captured using a simple optical phase contrast microscope to train CNNs. The results showed that the CNNs could be trained to recognize undifferentiated cells and differentiating cells with an accuracy of 0.961 on the independent test set. In addition, we found that CNNs successfully distinguished differentiated cells at a very early stage (only 1 day). Further analysis showed that overall morphological features of MSCs were the main basis for the CNN classification. In conclusion, MSCs differentiation detection can be achieved early and accurately through simple bright-field images and DL networks, which may also provide a potential and novel method for the field of cell detection in the near future.

SI-ViT: Shuffle instance-based Vision Transformer for pancreatic cancer ROSE image classification.

BACKGROUND AND OBJECTIVE: The rapid on-site evaluation (ROSE) technique improves pancreatic cancer diagnosis by enabling immediate analysis of fast-stained cytopathological images. Automating ROSE classification could not only reduce the burden on pathologists but also broaden the application of this increasingly popular technique. However, this approach faces substantial challenges due to complex perturbations in color distribution, brightness, and contrast, which are influenced by various staining environments and devices. Additionally, the pronounced variability in cancerous patterns across samples further complicates classification, underscoring the difficulty in precisely identifying local cells and establishing their global relationships. METHODS: To address these challenges, we propose an instance-aware approach that enhances the Vision Transformer with a novel shuffle instance strategy (SI-ViT). Our approach presents a shuffle step to generate bags of shuffled instances and corresponding bag-level soft-labels, allowing the model to understand relationships and distributions beyond the limited original distributions. Simultaneously, combined with an un-shuffle step, the traditional ViT can model the relationships corresponding to the sample labels. This dual-step approach helps the model to focus on inner-sample and cross-sample instance relationships, making it potent in extracting diverse image patterns and reducing complicated perturbations. RESULTS: Compared to state-of-the-art methods, significant improvements in ROSE classification have been achieved. Aiming for interpretability, equipped with instance shuffling, SI-ViT yields precise attention regions that identifying cancer and normal cells in various scenarios. Additionally, the approach shows excellent potential in pathological image analysis through generalization validation on other datasets. CONCLUSIONS: By proposing instance relationship modeling through shuffling, we introduce a new insight in pathological image analysis. The significant improvements in ROSE classification leads to protential AI-on-site applications in pancreatic cancer diagnosis. The code and results are publicly available at https://github.com/sagizty/MIL-SI.

Gardenia Iridoid Glucosides Protect Against α-Naphthalene Isothiocya-Nate-Induced Cholestatic Rats Through Activation of the FXR-SHP Signaling Pathway.

Introduction: Cholestasis is a common liver disorder that currently has limited treatment options. Gardenia Iridoid Glucosides (GIG) have been found to possess various physiological activities, such as cholagogic, hypoglycemic, antibacterial, and anti-inflammatory effects. The objective of this study was to investigate the effects of GIG on bile acid enterohepatic circulation and explore the underlying mechanism in cholestatic rats. Methods: In order to identify key pathways associated with cholestasis, we conducted Gene Ontology (GO) Enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. In vivo experiments were then performed on alpha-naphthylisothiocyanate (ANIT)-treated rats to assess the impact of GIG. We measured bile flow and various biomarkers including total bilirubin (TB), total bile acids (TBA), total cholesterol (TC), malondialdehyde (MDA), glutamic-pyruvic transaminase (GPT), glutamic oxaloacetic transaminase (GOT), and total superoxide dismutase (T-SOD) in the serum. We also examined the expression levels of bile salt export pump (BSEP), ATP-binding cassette subfamily B member 4 (ABCB4), far-nesoid X receptor (FXR), small heterodimer partner (SHP), cholesterol 7α-hydroxylase (CYP7A1), and sodium taurocholate cotransporting polypeptide (NTCP) in liver tissue. In vitro experiments were conducted on primary hepatocytes to further investigate the mechanism of action of GIG on the expression of SHP, CYP7A1, NTCP, and FXR. Results: Our in vivo experiments demonstrated that GIG significantly increased bile flow and reduced the levels of TB, TBA, TC, MDA, GPT, and GOT, while increasing T-SOD levels in ANIT-treated rats. Addi-tionally, GIG ameliorated liver tissue damage induced by ANIT, upregulated the expression of BSEP and ABCB4, and modulated the protein expression of FXR, SHP, CYP7A1, and NTCP in model rats. In vitro experiments further revealed that GIG inhibited the expression of SHP, CYP7A1, and NTCP by suppressing the expression of FXR. Conclusion: This study provides new insights into the therapeutic potential of GIG for the treatment of cholestasis. GIG demonstrated beneficial effects on bile acid enterohepatic circulation and liver biomarkers in cholestatic rats. The modulation of FXR and its downstream targets may contribute to the mechanism of action of GIG. These findings highlight the potential of GIG as a therapeutic intervention for cholangitis.

PPG-based Continuous BP Waveform Estimation Using Polarized Attention-guided Conditional Adversarial Learning Model.

The blood pressure (BP) waveform is a vital source of physiological and pathological information concerning the cardiovascular system. This study proposes a novel attention-guided conditional generative adversarial network (cGAN), named PPG2BP-cGAN, to estimate BP waveforms based on photoplethysmography (PPG) signals. The proposed model comprises a generator and a discriminator. Specifically, the UNet3+-based generator integrates a full-scale skip connection structure with a modified polarized self-attention module based on a spatial-temporal attention mechanism. Additionally, its discriminator comprises PatchGAN, which augments the discriminative power of the generated BP waveform by increasing the perceptual field through fully convolutional layers. We demonstrate the superior BP waveform prediction performance of our proposed method compared to state-of-the-art (SOTA) techniques on two independent datasets. Our approach first pre-trained on a dataset containing 683 subjects and then tested on a public dataset. Experimental results from the Multi-parameter Intelligent Monitoring in Intensive Care dataset show that the proposed method achieves a root mean square error of 3.54, mean absolute error of 2.86, and Pearson coefficient of 0.99 for BP waveform estimation. Furthermore, the estimation errors (mean error ± standard deviation error) for systolic BP and diastolic BP are 0.72 ± 4.34 mmHg and 0.41 ± 2.48 mmHg, respectively, meeting the American Association for the Advancement of Medical Instrumentation standard. Our approach exhibits significant superiority over SOTA techniques on independent datasets, thus highlighting its potential for future applications in continuous cuffless BP waveform measurement.

Two-stage deep learning method for sparse-view fluorescence molecular tomography reconstruction.

Fluorescence molecular tomography (FMT) is a preclinical optical tomographic imaging technique that can trace various physiological and pathological processes at the cellular or even molecular level. Reducing the number of FMT projection views can improve the data acquisition speed, which is significant in applications such as dynamic problems. However, a reduction in the number of projection views will dramatically aggravate the ill-posedness of the FMT inverse problem and lead to significant degradation of the reconstructed images. To deal with this problem, we have proposed a deep-learning-based reconstruction method for sparse-view FMT that only uses four perpendicular projection views and divides the image reconstruction into two stages: image restoration and inverse Radon transform. In the first stage, the projection views of the surface fluorescence are restored to eliminate the blur derived from photon diffusion through a fully convolutional neural network. In the second stage, another convolutional neural network is used to implement the inverse Radon transform between the restored projections from the first stage and the reconstructed transverse slices. Numerical simulation and phantom and mouse experiments are carried out. The results show that the proposed method can effectively deal with the image reconstruction problem of sparse-view FMT.

A Novel Feature Engineering Method Based on Latent Representation Learning for Radiomics: Application in NSCLC Subtype Classification.

Radiomics refers to the high-throughput extraction of quantitative features from medical images, and is widely used to construct machine learning models for the prediction of clinical outcomes, while feature engineering is the most important work in radiomics. However, current feature engineering methods fail to fully and effectively utilize the heterogeneity of features when dealing with different kinds of radiomics features. In this work, latent representation learning is first presented as a novel feature engineering approach to reconstruct a set of latent space features from original shape, intensity and texture features. This proposed method projects features into a subspace called latent space, in which the latent space features are obtained by minimizing a unique hybrid loss function including a clustering-like loss and a reconstruction loss. The former one ensures the separability among each class while the latter one narrows the gap between the original features and latent space features. Experiments were performed on a multi-center non-small cell lung cancer (NSCLC) subtype classification dataset from 8 international open databases. Results showed that compared with four traditional feature engineering methods (baseline, PCA, Lasso and L2,1-norm minimization), latent representation learning could significantly improve the classification performance of various machine learning classifiers on the independent test set (all p<0.001). Further on two additional test sets, latent representation learning also showed a significant improvement in generalization performance. Our research shows that latent representation learning is a more effective feature engineering method, which has the potential to be used as a general technology in a wide range of radiomics researches.

Spontaneous histological transformation of lung squamous-cell carcinoma to large cell neuroendocrine carcinoma and small cell lung cancer.

BACKGROUND: Histopathological transformation between different types of lung cancer cells has been reported following a variety of anti-tumor treatments. Examples include transformation from lung adenocarcinoma to squamous-cell carcinoma (SCC) and transformation from non-small cell lung cancer (NSCLC) to small cell lung cancer (SCLC). CASE REPORT: A patient with intermittent hemoptysis for 2 days underwent a computed tomography (CT) scan that revealed interstitial pneumonia in addition to two enlarged paratracheal lymph nodes: one on the right (4R) and one on the left (4L) measuring 10 and 7 mm in diameter, respectively (Fig. 1). There was no evidence of a lung or bronchial mass. Bronchoscopy identified an endoluminal primary mass in a superior segmental bronchus of the left lower lobe and pathological examination following surgery confirmed it to be SCC. At 15 months post operation, a CT scan detected that the 4R lymph node had increased in size from 10 to 16 mm in diameter. At the next follow-up 7 months later, a CT scan showed that the R4 lymph node had further increased in size from 16 to 40 mm in the short axis, making it difficult for a surgeon to resect it "en bloc" immediately. The maximum standardized uptake value was 7.5 on PET-CT images. One month following completion of one cycle of neoadjuvant chemotherapy with gemcitabine and nedaplatin, a further CT scan indicated that the lymph node had decreased in size from 40 to 30 mm in the short axis. A complete mediastinal lymphadenectomy via open thoracotomy was performed and the lymph node was resected. Histological examination identified a main large cell neuroendocrine carcinoma (LCNEC) component with a small fraction of small cell carcinoma, confirmed by immunohistochemical analysis and genetic evidence. CONCLUSION: Histopathological transformation from SCC to LCNEC with a small fraction of SCLC may have occurred spontaneously without any treatment.

Evaluation of humoral and cellular immune responses induced by a cocktail of recombinant African swine fever virus antigens fused with OprI in domestic pigs.

BACKGROUND: African swine fever (ASF) is a highly fatal disease in domestic pigs caused by ASF virus (ASFV), for which there is currently no commercial vaccine available. The genome of ASFV encodes more than 150 proteins, some of which have been included in subunit vaccines but only induce limited protection against ASFV challenge. METHODS: To enhance immune responses induced by ASFV proteins, we expressed and purified three fusion proteins with each consisting of bacterial lipoprotein OprI, 2 different ASFV proteins/epitopes and a universal CD4+ T cell epitope, namely OprI-p30-modified p54-TT, OprI-p72 epitopes-truncated pE248R-TT, and OprI-truncated CD2v-truncated pEP153R-TT. The immunostimulatory activity of these recombinant proteins was first assessed on dendritic cells. Then, humoral and cellular immunity induced by these three OprI-fused proteins cocktail formulated with ISA206 adjuvant (O-Ags-T formulation) were assessed in pigs. RESULTS: The OprI-fused proteins activated dendritic cells with elevated secretion of proinflammatory cytokines. Furthermore, the O-Ags-T formulation elicited a high level of antigen-specific IgG responses and interferon-γ-secreting CD4+ and CD8+ T cells after stimulation in vitro. Importantly, the sera and peripheral blood mononuclear cells from pigs vaccinated with the O-Ags-T formulation respectively reduced ASFV infection in vitro by 82.8% and 92.6%. CONCLUSIONS: Our results suggest that the OprI-fused proteins cocktail formulated with ISA206 adjuvant induces robust ASFV-specific humoral and cellular immune responses in pigs. Our study provides valuable information for the further development of subunit vaccines against ASF.

MSHT: Multi-stage Hybrid Transformer for the ROSE Image Analysis of Pancreatic Cancer.

Pancreatic cancer is one of the most malignant cancers with high mortality. The rapid on-site evaluation (ROSE) technique can significantly accelerate the diagnostic workflow of pancreatic cancer by immediately analyzing the fast-stained cytopathological images with on-site pathologists. However, the broader expansion of ROSE diagnosis has been hindered by the shortage of experienced pathologists. Deep learning has great potential for the automatic classification of ROSE images in diagnosis. But it is challenging to model the complicated local and global image features. The traditional convolutional neural network (CNN) structure can effectively extract spatial features, while it tends to ignore global features when the prominent local features are misleading. In contrast, the Transformer structure has excellent advantages in capturing global features and long-range relations, while it has limited ability in utilizing local features. We propose a multi-stage hybrid Transformer (MSHT) to combine the strengths of both, where a CNN backbone robustly extracts multi-stage local features at different scales as the attention guidance, and a Transformer encodes them for sophisticated global modeling. Going beyond the strength of each single method, the MSHT can simultaneously enhance the Transformer global modeling ability with the local guidance from CNN features. To evaluate the method in this unexplored field, a dataset of 4240 ROSE images is collected where MSHT achieves 95.68% in classification accuracy with more accurate attention regions. The distinctively superior results compared to the state-of-the-art models make MSHT extremely promising for cytopathological image analysis. The codes and records are available at: https://github.com/sagizty/ Multi-Stage-Hybrid-Transformer.

Structure and function of African swine fever virus proteins: Current understanding.

African swine fever virus (ASFV) is a highly infectious and lethal double-stranded DNA virus that is responsible for African swine fever (ASF). ASFV was first reported in Kenya in 1921. Subsequently, ASFV has spread to countries in Western Europe, Latin America, and Eastern Europe, as well as to China in 2018. ASFV epidemics have caused serious pig industry losses around the world. Since the 1960s, much effort has been devoted to the development of an effective ASF vaccine, including the production of inactivated vaccines, attenuated live vaccines, and subunit vaccines. Progress has been made, but unfortunately, no ASF vaccine has prevented epidemic spread of the virus in pig farms. The complex ASFV structure, comprising a variety of structural and non-structural proteins, has made the development of ASF vaccines difficult. Therefore, it is necessary to fully explore the structure and function of ASFV proteins in order to develop an effective ASF vaccine. In this review, we summarize what is known about the structure and function of ASFV proteins, including the most recently published findings.

Image restoration for blurry optical images caused by photon diffusion with deep learning.

Optical macroscopic imaging techniques have shown great significance in the investigations of biomedical issues by revealing structural or functional information of living bodies through the detection of visible or near-infrared light derived from different mechanisms. However, optical macroscopic imaging techniques suffer from poor spatial resolution due to photon diffusion in biological tissues. This dramatically restricts the application of optical imaging techniques in numerous situations. In this paper, an image restoration method based on deep learning is proposed to eliminate the blur caused by photon diffusion in optical macroscopic imaging. Two blurry images captured at orthogonal angles are used as the additional information to ensure the uniqueness of the solution and restore the small targets at deep locations. Then a fully convolutional neural network is proposed to accomplish the image restoration, which consists of three sectors: V-shaped network for central view, V-shaped network for side views, and synthetical path. The two V-shaped networks are concatenated to the synthetical path with skip connections to generate the output image. Simulations as well as phantom and mouse experiments are implemented. Results indicate the effectiveness of the proposed method.

Radiomics feature analysis and model research for predicting histopathological subtypes of non-small cell lung cancer on CT images: A multi-dataset study.

PURPOSE: Classifying the subtypes of non-small cell lung cancer (NSCLC) is essential for clinically adopting optimal treatment strategies and improving clinical outcomes, but the histological subtypes are confirmed by invasive biopsy or post-operative examination at present. Based on multi-center data, this study aimed to analyze the importance of extracted CT radiomics features and develop the model with good generalization performance for precisely distinguishing major NSCLC subtypes: adenocarcinoma (ADC) and squamous cell carcinoma (SCC). METHODS: We collected a multi-center CT dataset with 868 patients from eight international databases on the cancer imaging archive (TCIA). Among them, patients from five databases were mixed and split to training and test sets (560:140). The remaining three databases were used as independent test sets: TCGA set (n = 97) and lung3 set (n = 71). A total of 1409 features containing shape, intensity, and texture information were extracted from tumor volume of interest (VOI), then the ℓ2,1 -norm minimization was used for feature selection and the importance of selected features was analyzed. Next, the prediction and generalization performance of 130 radiomics models (10 common algorithms and 120 heterogeneous ensemble combinations) were compared by the average AUC value on three test sets. Finally, predictive results of the optimal model were shown. RESULTS: After feature selection, 401 features were obtained. Features of intensity, texture GLCM, GLRLM, and GLSZM had higher classification weight coefficients than other features (shape, texture GLDM, and NGTDM), and the filtered image features exhibited significant importance than original image features (p-value = 0.0210). Moreover, five ensemble learning algorithms (Bagging, AdaBoost, RF, XGBoost, GBDT) had better generalization performance (p-value = 0.00418) than other non-ensemble algorithms (MLP, LR, GNB, SVM, KNN). The Bagging-AdaBoost-SVM model had the highest AUC value (0.815 ± 0.010) on three test sets. It obtained AUC values of 0.819, 0.823, and 0.804 on test set, TCGA set and lung3 set, respectively. CONCLUSION: Our multi-dataset study showed that intensity features, texture features (GLCM, GLRLM, and GLSZM) and filtered image features were more important for distinguishing ADCs from SCCs. The method of ensemble learning can improve the prediction and generalization performance on the complicated multi-center data. The Bagging-AdaBoost-SVM model had the strongest generalization performance, and it showed promising clinical value for non-invasively predicting the histopathological subtypes of NSCLC.

Robust reconstruction of fluorescence molecular tomography based on adaptive adversarial learning strategy.

Objective.Fluorescence molecular tomography (FMT) is a promising molecular imaging modality for quantifying the three-dimensional (3D) distribution of tumor probes in small animals. However, traditional deep learning reconstruction methods that aim to minimize the mean squared error (MSE) and iterative regularization algorithms that rely on optimal parameters are typically influenced by strong noise, resulting in poor FMT reconstruction robustness.Approach.In this letter, we propose an adaptive adversarial learning strategy (3D-UR-WGAN) to achieve robust FMT reconstructions. Unlike the pixel-based MSE criterion in traditional CNNs or the regularization strategy in iterative solving schemes, the reconstruction strategy can greatly facilitate the performance of the network models through alternating loop training of the generator and the discriminator. Second, the reconstruction strategy combines the adversarial loss in GANs with the L1 loss to significantly enhance the robustness and preserve image details and textual information.Main results.Both numerical simulations and physical phantom experiments demonstrate that the 3D-UR-WGAN method can adaptively eliminate the effects of different noise levels on the reconstruction results, resulting in robust reconstructed images with reduced artifacts and enhanced image contrast. Compared with the state-of-the-art methods, the proposed method achieves better reconstruction performance in terms of target shape recovery and localization accuracy.Significance.This adaptive adversarial learning reconstruction strategy can provide a possible paradigm for robust reconstruction in complex environments, and also has great potential to provide an alternative solution for solving the problem of poor robustness encountered in other optical imaging modalities such as diffuse optical tomography, bioluminescence imaging, and Cherenkov luminescence imaging.

D2-RecST: Dual-domain joint reconstruction strategy for fluorescence molecular tomography based on image domain and perception domain.

BACKGROUND AND OBJECTIVE: Fluorescence molecular tomography (FMT) is a promising molecular imaging modality for quantifying the three-dimensional (3D) distribution of fluorescent probes in small animals. Over the past few years, learning-based FMT reconstruction methods have achieved promising results. However, these methods typically attempt to minimize the mean-squared error (MSE) between the reconstructed image and the ground truth. Although signal-to-noise ratios (SNRs) are improved, they are susceptible to non-uniform artifacts and loss of structural detail, making it extremely challenging to obtain accurate and robust FMT reconstructions under noisy measurements. METHODS: We propose a novel dual-domain joint strategy based on the image domain and perception domain for accurate and robust FMT reconstruction. First, we formulate an explicit adversarial learning strategy in the image domain, which greatly facilitates training and optimization through two enhanced networks to improve anti-noise ability. Besides, we introduce a novel transfer learning strategy in the perceptual domain to optimize edge details by providing perceptual priors for fluorescent targets. Collectively, the proposed dual-domain joint reconstruction strategy can significantly eliminate the non-uniform artifacts and effectively preserve the structural edge details. RESULTS: Both numerical simulations and in vivo mouse experiments demonstrate that the proposed method markedly outperforms traditional and cutting-edge methods in terms of positioning accuracy, image contrast, robustness, and target morphological recovery. CONCLUSIONS: The proposed method achieves the best reconstruction performance and has great potential to facilitate precise localization and 3D visualization of tumors in in vivo animal experiments.

KD-Informer: A Cuff-Less Continuous Blood Pressure Waveform Estimation Approach Based on Single Photoplethysmography.

Ambulatory blood pressure (BP) monitoring plays a critical role in the early prevention and diagnosis of cardiovascular diseases. However, cuff-based inflatable devices cannot be used for continuous BP monitoring, while pulse transit time or multi-parameter-based methods require more bioelectrodes to acquire electrocardiogram signals. Thus, estimating the BP waveforms only based on photoplethysmography (PPG) signals for continuous BP monitoring has essential clinical values. Nevertheless, extracting useful features from raw PPG signals for fine-grained BP waveform estimation is challenging due to the physiological variation and noise interference. For single PPG analysis utilizing deep learning methods, the previous works depend mainly on stacked convolution operation, which ignores the underlying complementary time-dependent information. Thus, this work presents a novel Transformer-based method with knowledge distillation (KD-Informer) for BP waveform estimation. Meanwhile, we integrate the prior information of PPG patterns, selected by a novel backward elimination algorithm, into the knowledge transfer branch of the KD-Informer. With these strategies, the model can effectively capture the discriminative features through a lightweight architecture during the learning process. Then, we further adopt an effective transfer learning technique to demonstrate the excellent generalization capability of the proposed model using two independent multicenter datasets. Specifically, we first fine-tuned the KD-Informer with a large and high-quality dataset (Mindray dataset) and then transferred the pre-trained model to the target domain (MIMIC dataset). The experimental test results on the MIMIC dataset showed that the KD-Informer exhibited an estimation error of 0.02 ± 5.93 mmHg for systolic BP (SBP) and 0.01 ± 3.87 mmHg for diastolic BP (DBP), which complied with the association for the advancement of medical instrumentation (AAMI) standard. These results demonstrate that the KD-Informer has high reliability and elegant robustness to measure continuous BP waveforms.

Analgesic effect of butorphanol tartrate combined with hydromorphone on patients with cesarean section: A prospective cohort study.

To evaluate the analgesic effect of butorphanol tartrate combined with hydromorphone on the patients with cesarean section, we conducted a prospective cohort study. A total of 90 patients were given patient-controlled intravenous analgesia (PCIA) with hydromorphone for 24 hours after the cesarean section. After stopping PCIA, they were divided into 2 groups randomly. The cases treated with butorphanol tartrate intravenous drip were evaluated as the butorphanol group (n = 45) and the cases treated with saline were evaluated as the control group (n = 45). We compared the vital signs, analgesic effect, adverse reactions, the bladder and gastrointestinal function recovery, and neonatal jaundice between the 2 groups. The visual analog score in butorphanol group was significantly lower than that of control group at 3 and 4 hours after stopping PCIA (P < .05), but there was no significant difference in visual analog score at 6 and 12 hours after stopping PCIA. The first time of getting out of bed and urination in butorphanol group was significantly later than that in control group while there was no significant difference in the first anal ventilation and the neonatal jaundice index between the 2 groups. We should pay attention to the pain of patients with cesarean section after stopping PCIA. The combination of butorphanol tartrate and hydromorphone play a good effect to relieve the pain while nursing care should be strengthened to urge patients to take early activities to reduce the occurrence of urinary retention.

Multi-branch attention prior based parameterized generative adversarial network for fast and accurate limited-projection reconstruction in fluorescence molecular tomography.

Limited-projection fluorescence molecular tomography (FMT) allows rapid reconstruction of the three-dimensional (3D) distribution of fluorescent targets within a shorter data acquisition time. However, the limited-projection FMT is severely ill-posed and ill-conditioned due to insufficient fluorescence measurements and the strong scattering properties of photons in biological tissues. Previously, regularization-based methods, combined with the sparse distribution of fluorescent sources, have been commonly used to alleviate the severe ill-posed nature of the limited-projection FMT. Due to the complex iterative computations, time-consuming solution procedures, and less stable reconstruction results, the limited-projection FMT remains an intractable challenge for achieving fast and accurate reconstructions. In this work, we completely discard the previous iterative solving-based reconstruction themes and propose multi-branch attention prior based parameterized generative adversarial network (MAP-PGAN) to achieve fast and accurate limited-projection FMT reconstruction. Firstly, the multi-branch attention can provide parameterized weighted sparse prior information for fluorescent sources, enabling MAP-PGAN to effectively mitigate the ill-posedness and significantly improve the reconstruction accuracy of limited-projection FMT. Secondly, since the end-to-end direct reconstruction strategy is adopted, the complex iterative computation process in traditional regularization algorithms can be avoided, thus greatly accelerating the 3D visualization process. The numerical simulation results show that the proposed MAP-PGAN method outperforms the state-of-the-art methods in terms of localization accuracy and morphological recovery. Meanwhile, the reconstruction time is only about 0.18s, which is about 100 to 1000 times faster than the conventional iteration-based regularization algorithms. The reconstruction results from the physical phantoms and in vivo experiments further demonstrate the feasibility and practicality of the MAP-PGAN method in achieving fast and accurate limited-projection FMT reconstruction.