An n-Dimensional Chaotic Map with Application in Reversible Data Hiding for Medical Images.

The drawbacks of a one-dimensional chaotic map are its straightforward structure, abrupt intervals, and ease of signal prediction. Richer performance and a more complicated structure are required for multidimensional chaotic mapping. To address the shortcomings of current chaotic systems, an n-dimensional cosine-transform-based chaotic system (nD-CTBCS) with a chaotic coupling model is suggested in this study. To create chaotic maps of any desired dimension, nD-CTBCS can take advantage of already-existing 1D chaotic maps as seed chaotic maps. Three two-dimensional chaotic maps are provided as examples to illustrate the impact. The findings of the evaluation and experiments demonstrate that the newly created chaotic maps function better, have broader chaotic intervals, and display hyperchaotic behavior. To further demonstrate the practicability of nD-CTBCS, a reversible data hiding scheme is proposed for the secure communication of medical images. The experimental results show that the proposed method has higher security than the existing methods.

SM-GRSNet: sparse mapping-based graph representation segmentation network for honeycomb lung lesion.

Objective. Honeycomb lung is a rare but severe disease characterized by honeycomb-like imaging features and distinct radiological characteristics. Therefore, this study aims to develop a deep-learning model capable of segmenting honeycomb lung lesions from Computed Tomography (CT) scans to address the efficacy issue of honeycomb lung segmentation.Methods. This study proposes a sparse mapping-based graph representation segmentation network (SM-GRSNet). SM-GRSNet integrates an attention affinity mechanism to effectively filter redundant features at a coarse-grained region level. The attention encoder generated by this mechanism specifically focuses on the lesion area. Additionally, we introduce a graph representation module based on sparse links in SM-GRSNet. Subsequently, graph representation operations are performed on the sparse graph, yielding detailed lesion segmentation results. Finally, we construct a pyramid-structured cascaded decoder in SM-GRSNet, which combines features from the sparse link-based graph representation modules and attention encoders to generate the final segmentation mask.Results. Experimental results demonstrate that the proposed SM-GRSNet achieves state-of-the-art performance on a dataset comprising 7170 honeycomb lung CT images. Our model attains the highest IOU (87.62%), Dice(93.41%). Furthermore, our model also achieves the lowest HD95 (6.95) and ASD (2.47).Significance.The SM-GRSNet method proposed in this paper can be used for automatic segmentation of honeycomb lung CT images, which enhances the segmentation performance of Honeycomb lung lesions under small sample datasets. It will help doctors with early screening, accurate diagnosis, and customized treatment. This method maintains a high correlation and consistency between the automatic segmentation results and the expert manual segmentation results. Accurate automatic segmentation of the honeycomb lung lesion area is clinically important.

Novel JAG1 variants leading to Alagille syndrome in two Chinese cases.

Alagille Syndrome (ALGS) is a complex genetic disorder characterized by cholestasis, congenital cardiac anomalies, and butterfly vertebrae. The variable phenotypic expression of ALGS can lead to challenges in accurately diagnosing affected infants, potentially resulting in misdiagnoses or underdiagnoses. This study highlights novel JAG1 gene mutations in two cases of ALGS. The first case with a novel p.Pro325Leufs*87 variant was diagnosed at 2 months of age and exhibited a favorable prognosis and an unexpected manifestation of congenital hypothyroidism. Before the age of 2, the second patient was incorrectly diagnosed with liver structural abnormalities, necessitating extensive treatment. In addition, he exhibited delays in language acquisition that may have been a result of SNAP25 haploinsufficiency. The identification of ALGS remains challenging, highlighting the importance of early detection and genetic testing for effective patient management. The variant p.Pro325Leufs*87 is distinct from reported variants linked to congenital hypothyroidism in ALGS patients, thereby further confirming the clinical and genetic complexity of ALGS. This emphasizes the critical need for individualized and innovative approaches to diagnosis and medical interventions, uniquely intended to address the complexity of this syndrome.

SELDNet: Sequenced encoder and lightweight decoder network for COVID-19 infection region segmentation.

Segmenting regions of lung infection from computed tomography (CT) images shows excellent potential for rapid and accurate quantifying of Coronavirus disease 2019 (COVID-19) infection and determining disease development and treatment approaches. However, a number of challenges remain, including the complexity of imaging features and their variability with disease progression, as well as the high similarity to other lung diseases, which makes feature extraction difficult. To answer the above challenges, we propose a new sequence encoder and lightweight decoder network for medical image segmentation model (SELDNet). (i) Construct sequence encoders and lightweight decoders based on Transformer and deep separable convolution, respectively, to achieve different fine-grained feature extraction. (ii) Design a semantic association module based on cross-attention mechanism between encoder and decoder to enhance the fusion of different levels of semantics. The experimental results showed that the network can effectively achieve segmentation of COVID-19 infected regions. The dice of the segmentation result was 79.1%, the sensitivity was 76.3%, and the specificity was 96.7%. Compared with several state-of-the-art image segmentation models, our proposed SELDNet model achieves better results in the segmentation task of COVID-19 infected regions.

COVID-19 CT image recognition algorithm based on transformer and CNN.

Novel corona virus pneumonia (COVID-19) broke out in 2019, which had a great impact on the development of world economy and people's lives. As a new mainstream image processing method, deep learning network has been constructed to extract medical features from chest CT images, and has been used as a new detection method in clinical practice. However, due to the medical characteristics of COVID-19 CT images, the lesions are widely distributed and have many local features. Therefore, it is difficult to diagnose directly by using the existing deep learning model. According to the medical features of CT images in COVID-19, a parallel bi-branch model (Trans-CNN Net) based on Transformer module and Convolutional Neural Network module is proposed by making full use of the local feature extraction capability of Convolutional Neural Network and the global feature extraction advantage of Transformer. According to the principle of cross-fusion, a bi-directional feature fusion structure is designed, in which features extracted from two branches are fused bi-directionally, and the parallel structures of branches are fused by a feature fusion module, forming a model that can extract features of different scales. To verify the effect of network classification, the classification accuracy on COVIDx-CT dataset is 96.7%, which is obviously higher than that of typical CNN network (ResNet-152) (95.2%) and Transformer network (Deit-B) (75.8%). These results demonstrate accuracy is improved. This model also provides a new method for the diagnosis of COVID-19, and through the combination of deep learning and medical imaging, it promotes the development of real-time diagnosis of lung diseases caused by COVID-19 infection, which is helpful for reliable and rapid diagnosis, thus saving precious lives.

Biosynthesis of sorafenib coated graphene nanosheets for the treatment of gastric cancer in patients in nursing care.

Sorafenib (SRF) is a well-known tyrosine kinase inhibiting anticancer drug which iseffectual against multiple carcinomas especially gastric cancers by targeting the Ras/Raf/Mek/Erk cascade pathway and blocking the tumor cell proliferation. In the present work, we have reduced graphene oxide (GO) in presence of sorafenib using ascorbic as green reducing agent for the treatment of gastric cancers. Sorafenib reduced graphene oxide (SRGO) were obtained with a transparent and smoothmorphology. The drug loaded SRGO has presented significant cytotoxic effect against SGC7901 cancer cells when compared to that of the free SRF and blank NPs in the equivalent concentrations. Additionally, from the Hoechst 33382 staining study it was evident that the cells in untreated groups remained intact with its round shape and intact nuclei while the SRGO treated cells have shown a cell transformation with apoptosis of gastric cancer cell lines. Based on these results, we can conclude that SRGO might extend an enormous prospective in the treatment of gastric cancers.

Transforming growth factor-ß1 gene polymorphisms with liver cirrhosis risk: A meta-analysis.

Although several epidemiological studies have investigated the association of transforming growth factor-ß1 (TGF-ß1) gene polymorphisms with the susceptibility to liver cirrhosis (LC), controversial results exist. Consequently, we performed a meta-analysis to accurately evaluate the relationship of TGF-ß1-509C/T and codon 10T/C polymorphisms with the risk of LC introduced by chronic hepatitis B/V virus (HBV/HCV) infection. A total of 9 case-control studies, involving 985 LC patients and 909 controls, were recruited for meta-analysis. The results suggested a significant association between the -509C/T polymorphism and LC risk in the total population. Stratification by ethnicity revealed similar associations in Egyptian and Caucasian populations, but not in Asian populations. Subgroup analyses by different etiologies also showed similar associations in HCV-induced LC, but not in HBV-induced LC. However, the overall data failed to show a significant association between codon 10T/C polymorphism and the risk of LC in the study. We concluded that TGF-ß1-509C/T polymorphism was significantly associated with LC susceptibility, while the codon 10T/C polymorphism seemed to have a limited role in predicting the occurrence of LC induced by HBV/HCV infection.