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.

The accurate segmentation of multiple types of lesions from adjacent tissues in medical images is significant in clinical practice. Convolutional neural networks (CNNs) based on the coarse-to-fine strategy have been widely used in this field. However, multi-lesion segmentation remains to be challenging due to the uncertainty in size, contrast, and high interclass similarity of tissues. In addition, the commonly adopted cascaded strategy is rather demanding in terms of hardware, which limits the potential of clinical deployment. To address the problems above, we propose a novel Prior Attention Network (PANet) that follows the coarse-to-fine strategy to perform multi-lesion segmentation in medical images. The proposed network achieves the two steps of segmentation in a single network by inserting a lesion-related spatial attention mechanism in the network. Further, we also propose the intermediate supervision strategy for generating lesion-related attention to acquire the regions of interest (ROIs), which accelerates the convergence and obviously improves the segmentation performance. We have investigated the proposed segmentation framework in two applications: 2D segmentation of multiple lung infections in lung CT slices and 3D segmentation of multiple lesions in brain MRIs. Experimental results show that in both 2D and 3D segmentation tasks our proposed network achieves better performance with less computational cost compared with cascaded networks. The proposed network can be regarded as a universal solution to multi-lesion segmentation in both 2D and 3D tasks. The source code is available at https: //github.com/hsiangyuzhao/PANet.

Histopathological examinations heavily rely on hematoxylin and eosin (HE) and immunohistochemistry (IHC) staining. IHC staining can offer more accurate diagnostic details but it brings significant financial and time costs. Furthermore, either re-staining HE-stained slides or using adjacent slides for IHC may compromise the accuracy of pathological diagnosis due to information loss. To address these challenges, we develop PST-Diff, a method for generating virtual IHC images from HE images based on diffusion models, which allows pathologists to simultaneously view multiple staining results from the same tissue slide. To maintain the pathological consistency of the stain transfer, we propose the asymmetric attention mechanism (AAM) and latent transfer (LT) module in PST-Diff. Specifically, the AAM can retain more local pathological information of the source domain images through the design of asymmetric attention mechanisms, while ensuring the model’s flexibility in generating virtual stained images that highly confirm to the target domain. Subsequently, the LT module transfers the implicit representations across different domains, effectively alleviating the bias introduced by direct connection and further enhancing the pathological consistency of PST-Diff. Furthermore, to maintain the structural consistency of the stain transfer, the conditional frequency guidance (CFG) module is proposed to precisely control image generation and preserve structural details according to the frequency recovery process. To conclude, the pathological and structural consistency constraints provide PST-Diff with effectiveness and superior generalization in generating stable and functionally pathological IHC images with the best evaluation score. In general, PST-Diff offers prospective application in clinical virtual staining and pathological image analysis.

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.

Polymeric nanoparticles were widely used as delivery vehicles for targeted delivery of anticancer drugs, because of their targeting property and versatility. Mitochondria are one of the important organelles that regulate the apoptosis of cancer cells and can be considered as a pivotal target for cancer treatment. A pH-responsive charge-reversal and mitochondrial targeting nanoparticles, Vitamin B6-oligomeric hyaluronic acid-dithiodipropionic acid-berberine (B6-oHA-SS-Ber), were prepared in this study. Ber is a lipophilic cation that was conjugated with oHA through disulfide bonds to produce mitochondria-targeted conjugates (oHA-SS-Ber). B6 was conjugated to oHA to obtain B6-oHA-SS-Ber and the two types of Cur-loaded nanoparticles (Cur-NPs) were formulated by the dialysis method. Due to pKa of B6, the charge they carried in the tumor tissue acidic microenvironment can be transferred from negative charge to positive charge, further targeting mitochondria. In our study, we successfully synthesized B6-HA-SS-Ber and characterized the structure by ¹H-NMR. According to the results of transmission electron microscopy (TEM), we found that the B6-oHA-SS-Ber/Cur micelles could self-assembled in water to form spherical nanoparticles, with a hydrodynamic diameter of 172.9±13 nm. Moreover, in vitro cytotoxicity, cellular uptake, lysosome escape and mitochondrial distribution researches revealed the better effect of B6-oHA-SS-Ber/Cur micelles in comparison to oHA-SS-Ber/Cur. In vivo anticancer activities indicated that the B6-oHA-SS-Ber/Cur micelles exhibited effective inhibition of tumor growth.

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.