Manual segmentation of coronary arteries in computed tomography angiography (CTA) images is inefficient, and existing deep learning segmentation models often exhibit low accuracy on coronary artery images. Inspired by the Transformer architecture, this paper proposes a novel segmentation model, the double parallel encoder u-net with transformers (DUNETR). This network employed a dual-encoder design integrating Transformers and convolutional neural networks (CNNs). The Transformer encoder transformed three-dimensional (3D) coronary artery data into a one-dimensional (1D) sequential problem, effectively capturing global multi-scale feature information. Meanwhile, the CNN encoder extracted local features of the 3D coronary arteries. The complementary features extracted by the two encoders were fused through the noise reduction feature fusion (NRFF) module and passed to the decoder. Experimental results on a public dataset demonstrated that the proposed DUNETR model achieved a Dice similarity coefficient of 81.19% and a recall rate of 80.18%, representing improvements of 0.49% and 0.46%, respectively, over the next best model in comparative experiments. These results surpassed those of other conventional deep learning methods. The integration of Transformers and CNNs as dual encoders enables the extraction of rich feature information, significantly enhancing the effectiveness of 3D coronary artery segmentation. Additionally, this model provides a novel approach for segmenting other vascular structures.
In order to address the issues of spatial induction bias and lack of effective representation of global contextual information in colon polyp image segmentation, which lead to the loss of edge details and mis-segmentation of lesion areas, a colon polyp segmentation method that combines Transformer and cross-level phase-awareness is proposed. The method started from the perspective of global feature transformation, and used a hierarchical Transformer encoder to extract semantic information and spatial details of lesion areas layer by layer. Secondly, a phase-aware fusion module (PAFM) was designed to capture cross-level interaction information and effectively aggregate multi-scale contextual information. Thirdly, a position oriented functional module (POF) was designed to effectively integrate global and local feature information, fill in semantic gaps, and suppress background noise. Fourthly, a residual axis reverse attention module (RA-IA) was used to improve the network’s ability to recognize edge pixels. The proposed method was experimentally tested on public datasets CVC-ClinicDB, Kvasir, CVC-ColonDB, and EITS, with Dice similarity coefficients of 94.04%, 92.04%, 80.78%, and 76.80%, respectively, and mean intersection over union of 89.31%, 86.81%, 73.55%, and 69.10%, respectively. The simulation experimental results show that the proposed method can effectively segment colon polyp images, providing a new window for the diagnosis of colon polyps.
Protein lysine β-hydroxybutyrylation (Kbhb) is a newly discovered post-translational modification associated with a wide range of biological processes. Identifying Kbhb sites is critical for a better understanding of its mechanism of action. However, biochemical experimental methods for probing Kbhb sites are costly and have a long cycle. Therefore, a feature embedding learning method based on the Transformer encoder was proposed to predict Kbhb sites. In this method, amino acid residues were mapped into numerical vectors according to their amino acid class and position in a learnable feature embedding method. Then the Transformer encoder was used to extract discriminating features, and the bidirectional long short-term memory network (BiLSTM) was used to capture the correlation between different features. In this paper, a benchmark dataset was constructed, and a Kbhb site predictor, AutoTF-Kbhb, was implemented based on the proposed method. Experimental results showed that the proposed feature embedding learning method could extract effective features. AutoTF-Kbhb achieved an area under curve (AUC) of 0.87 and a Matthews correlation coefficient (MCC) of 0.37 on the independent test set, significantly outperforming other methods in comparison. Therefore, AutoTF-Kbhb can be used as an auxiliary means to identify Kbhb sites.
Medical cross-modal retrieval aims to achieve semantic similarity search between different modalities of medical cases, such as quickly locating relevant ultrasound images through ultrasound reports, or using ultrasound images to retrieve matching reports. However, existing medical cross-modal hash retrieval methods face significant challenges, including semantic and visual differences between modalities and the scalability issues of hash algorithms in handling large-scale data. To address these challenges, this paper proposes a Medical image Semantic Alignment Cross-modal Hashing based on Transformer (MSACH). The algorithm employed a segmented training strategy, combining modality feature extraction and hash function learning, effectively extracting low-dimensional features containing important semantic information. A Transformer encoder was used for cross-modal semantic learning. By introducing manifold similarity constraints, balance constraints, and a linear classification network constraint, the algorithm enhanced the discriminability of the hash codes. Experimental results demonstrated that the MSACH algorithm improved the mean average precision (MAP) by 11.8% and 12.8% on two datasets compared to traditional methods. The algorithm exhibits outstanding performance in enhancing retrieval accuracy and handling large-scale medical data, showing promising potential for practical applications.
Accurate detection of cephalometric landmarks is crucial for orthodontic diagnosis and treatment planning. Current landmark detection methods are mainly divided into heatmap-based and regression-based approaches. However, these methods often rely on parallel computation of multiple models to improve accuracy, significantly increasing the complexity of training and deployment. This paper presented a novel regression method that can simultaneously detect all cephalometric landmarks in high-resolution X-ray images. By leveraging the encoder module of Transformer, a dual-encoder model was designed to achieve coarse-to-fine localization of cephalometric landmarks. The entire model consisted of three main components: a feature extraction module, a reference encoder module, and a fine-tuning encoder module, responsible for feature extraction and fusion of X-ray images, coarse localization of cephalometric landmarks, and fine localization of landmarks, respectively. The model was fully end-to-end differentiable and could learn the intercorrelation relationships between cephalometric landmarks. Experimental results showed that the successful detection rate (SDR) of our algorithm was superior to other existing methods. It attained the highest 2 mm SDR of 89.51% on test set 1 of the ISBI2015 dataset and 90.68% on the test set of the ISBI2023 dataset. Meanwhile, it reduces memory consumption and enhances the model’s popularity and applicability, providing more reliable technical support for orthodontic diagnosis and treatment plan formulation.
The synergistic effect of drug combinations can solve the problem of acquired resistance to single drug therapy and has great potential for the treatment of complex diseases such as cancer. In this study, to explore the impact of interactions between different drug molecules on the effect of anticancer drugs, we proposed a Transformer-based deep learning prediction model—SMILESynergy. First, the drug text data—simplified molecular input line entry system (SMILES) were used to represent the drug molecules, and drug molecule isomers were generated through SMILES Enumeration for data augmentation. Then, the attention mechanism in the Transformer was used to encode and decode the drug molecules after data augmentation, and finally, a multi-layer perceptron (MLP) was connected to obtain the synergy value of the drugs. Experimental results showed that our model had a mean squared error of 51.34 in regression analysis, an accuracy of 0.97 in classification analysis, and better predictive performance than the DeepSynergy and MulinputSynergy models. SMILESynergy offers improved predictive performance to assist researchers in rapidly screening optimal drug combinations to improve cancer treatment outcomes.
Leukemia is a common, multiple and dangerous blood disease, whose early diagnosis and treatment are very important. At present, the diagnosis of leukemia heavily relies on morphological examination of blood cell images by pathologists, which is tedious and time-consuming. Meanwhile, the diagnostic results are highly subjective, which may lead to misdiagnosis and missed diagnosis. To address the gap above, we proposed an improved Vision Transformer model for blood cell recognition. First, a faster R-CNN network was used to locate and extract individual blood cell slices from original images. Then, we split the single-cell image into multiple image patches and put them into the encoder layer for feature extraction. Based on the self-attention mechanism of the Transformer, we proposed a sparse attention module which could focus on the discriminative parts of blood cell images and improve the fine-grained feature representation ability of the model. Finally, a contrastive loss function was adopted to further increase the inter-class difference and intra-class consistency of the extracted features. Experimental results showed that the proposed module outperformed the other approaches and significantly improved the accuracy to 91.96% on the Munich single-cell morphological dataset of leukocytes, which is expected to provide a reference for physicians’ clinical diagnosis.
OBJECTIVE: To explore the relationship between characteristics of transformed cell and tumorigenicity. METHODS: Documents about transformed cell and tumorigenicity were reviewed in detail. RESULTS: Normal biological characteristics and cell function could be maintained in non-tumorigenic transformed cell, but it was changed markedly in malignant transformed cell. CONCLUSION: Non-tumorigenic transformed cell can be served as a standard cell line to study the function and growth characteristics of normal cell.
Objective To determine whether the transforminggrowth factor β1 (TGF-β1) is a key regulatory molecule required for an increase or a balance of extracellular matrix (ECM) and DNA synthesis in the goat passaged nucleus pulposus (NP) cells. Methods The NP cells isolated from the goat intervertebral discs were cultured in vitro for a serial of passages and transfected with the replicationincompetent adenoviral vectors carrying the human TGF-β1 (hTGF-β1) or lacZ genes. Then, they were cultured in monolayer or alginate bead 3dimensional (3-D) systems for 10 days.The changes in the production and the molecular components of ECM that occurredin the NP cells transfected with Ad/hTGF-β1 or the controls were evaluated by Westernblot and absorbance of glycosaminoglycan (GAG)-Alcian Blue complexes. Differences of DNA synthesis in the variant cells and culture systems were assessed by fluorometric analysis of the DNA content. ResultsA quantitation in the variant culture systems indicated that in monolayers the NP cells at Passage 3 transfected with Ad/hTGF-β1 had a much higher cell viability and more DNA synthesis(P＜0.05); however, in the alginate 3-D culture system, the NP cells transfected with Ad/hTGF-β1 did not have any significant difference from the controls(P＞0.05). The Western blotting analysis ofthe protein sample isolated from the variant cells for TGF-β1, type Ⅱ collagen, and Aggrecan expression indicated that in the monolayers and alginate 3-D culture systems the NP cells at Passage 3 transfected with Ad/hTGF-β1 revealed much higher protein levels than the controls(P＜0.05)； whereas the type Ⅰcollagen content was much lower than the controls (P＜0.05), but a significatly increased ratio of type Ⅱ/type Ⅰ collagen was found in both of the cell culture systems(P＜0.05). The GAG quantification also showed a positive result in both the cell culture systems and the NP cells at Passage 3 transfected with Ad/hTGF-β1 had a much higher GAG content than the controls(P＜0.05). Conclusion To a greaterextent, hTGF-β1 can play a key role in maintaining the phenotype of the NP cells and can still have an effect of the phenotypic modulation after a serial of the cell passages. The NP cells that are genetically manipulated to express hTGF-β1 have a promising effect on the restoration of the intervertebral disc defects. The NP cells transfected with Ad/hTGF-β1 cultured in the 3-D alginate bead systems can show a nearly native phenotype.
OBJECTIVE: To investigate the effects of basic fibroblast growth factor (bFGF) on the promoter activities of human alpha 1(I) procollagen gene and the interaction between bFGF and transforming growth factor-beta 1 (TGF-beta 1). METHODS: Fibroblasts of the hypertrophic scar and normal skin from a 3-year-old patient were primarily cultured and subcultured in vitro. Both of the fibroblasts were transient transfected with phCOL 2.5, containing -2.5 kb of 5’f lank sequence of human alpha 1(I) procollagen gene and CAT reporter gene by FuGENE transfection reagent; and treated thereafter by 16 ng/ml bFGF, 2 ng/ml TGF-beta 1 and 16 ng/ml bFGF + 2 ng/ml TGF beta 1 for 24 hours. The relative CAT expression values were determined by CAT-ELISA. RESULTS: TGF-beta 1 bly induced the CAT expression level, however, bFGF not only inhibited the basal CAT expression but also reduced the CAT expression up-regulated by TGF-beta 1 in normal skin and hypertrophic scar fibroblasts (P lt; 0.05). CONCLUSION: bFGF can reduce the promoter activities of human alpha 1(I) procollagen gene and antagonize the role of TGF-beta 1 in up-regulating the promoter activities of human alpha 1(I) procollagen gene in normal skin and hyertrophic scar fibroblasts.
