The research on medical image segmentation is of great significance in advancing efficient and accurate automated image processing techniques.To address the problem of inaccurate segmentation results caused by significant variations in organ tissue shapes and blurred boundaries present in medical images,a novel network named MFMANet is proposed.Built upon a"U"-shaped architecture,the network integrates multi-scale information fusion modules and multi-attention modules.Specifically,multi-scale information modules capture multi-scale information in the shallow layers of the network to bridge the semantic gap between encoder and decoder features,thereby enhancing the network's ability to handle large variations in organ sizes.Regarding the issue of blurred boundaries,multi-attention mechanism utilizes Swin Transformer as the deep encoder-decoder network,employing channel and spatial attention instead of traditional skip connections to achieve finer feature extraction.Experimental results on the ACDC and Synapse public datasets show that the proposed method achieves improvements of 1.51%and 1.29%in Dice similarity coefficient as compared with MTUNet,fully demonstrating its effectiveness in enhancing segmentation network accuracy.

The research on medical image segmentation is of great significance in advancing efficient and accurate automated image processing techniques.To address the problem of inaccurate segmentation results caused by significant variations in organ tissue shapes and blurred boundaries present in medical images,a novel network named MFMANet is proposed.Built upon a"U"-shaped architecture,the network integrates multi-scale information fusion modules and multi-attention modules.Specifically,multi-scale information modules capture multi-scale information in the shallow layers of the network to bridge the semantic gap between encoder and decoder features,thereby enhancing the network's ability to handle large variations in organ sizes.Regarding the issue of blurred boundaries,multi-attention mechanism utilizes Swin Transformer as the deep encoder-decoder network,employing channel and spatial attention instead of traditional skip connections to achieve finer feature extraction.Experimental results on the ACDC and Synapse public datasets show that the proposed method achieves improvements of 1.51%and 1.29%in Dice similarity coefficient as compared with MTUNet,fully demonstrating its effectiveness in enhancing segmentation network accuracy.

A U-Net incorporating improved Transformer and convolutional channel attention module is designed for biventricular segmentation in MRI image.By replacing the high-level convolution of U-Net with the improved Transformer,the global feature information can be effectively extracted to cope with the challenge of poor segmentation performance due to the complex morphological variation of the right ventricle.The improved Transformer incorporates a fixed window attention for position localization in the self-attention module,and aggregates the output feature map for reducing the feature map size;and the network learning capability is improved by increasing network depth through the adjustment of multilayer perceptron.To solve the problem of unsatisfactory segmentation performance caused by blurred tissue edges,a feature aggregation module is used for the fusion of multi-level underlying features,and a convolutional channel attention module is adopted to rescale the underlying features to achieve adaptive learning of feature weights.In addition,a plug-and-play feature enhancement module is integrated to improve the segmentation performance which is affected by feature loss due to channel decay in the codec structure,which guarantees the spatial information while increasing the proportion of useful channel information.The test on the ACDC dataset shows that the proposed method has higher biventricular segmentation accuracy,especially for the right ventricle segmentation.Compared with other methods,the proposed method improves the DSC coefficient by at least 2.83%,proving its effectiveness in biventricular segmentation.

Ureaplasma is a common pathogen in the human reproductive tract and consists of two distinct biotypes: biotype 1 and biotype 2. In 2002, based on the differences between biotypes, biotype 1 was further classified to a separate species named Ureaplasma parvum (Up), whereas biotype 2 is referred to as Ureaplasma urealyticum (Uu). Uu infection is associated with various urogenital diseases including infertility, preterm birth, and urethritis, while the pathogenicity of Up remains controversial. Researches have shown that different serotypes showed distinct pathogenicity and drug resistance in different diseases and populations, highlighting the importance of clinical tests of serotype and biotype for Ureaplasma. This article reviews the factors that may be associated with Ureaplasma infection, and the current status of the diagnosis and treatment in clinical practice, aiming to provide insights into the clinical significance and necessity of biotypes and serotype tests for Ureaplasma-positive cases and to serve as a reference for the clinical diagnosis and treatment of related diseases.

Objective:To investigate the characteristics of cervical microbiota in patients with HPV (Human Papillomavirus) infection, and to analyze the associations of cervical microbiota and HPV infection or cervicitis.Methods:300 samples underwent HPV nucleic acid testing was collected in this case-control study from June 2019 to April 2020 in the Zhujiang Hospital of Southern Medical University, there were 150 cases allocated in HPV infection group (HPV+), and 150 cases of negative nucleic acid test were non-infectious Group (HPV-). Next-generation sequencing was used to sequence the V4 region of the bacterial 16S rRNA gene, and QIIME pipeline was used to analysis the microbiota composition of the two groups. Wilcoxon rank sum test and Kruskal-Wallis test were used to statistically analysis the differences of the microbiota between groups; and the α diversity and β diversity of the flora between groups were statistically analyzed by Adonis multivariate analysis of variance and Wilcoxon rank sum test.Results:A total of 300 samples were analyzed in this study, of which 150 samples were HPV-positive and 150 samples were HPV-negative; among HPV-positive cases, 132 were infected by high-risk HPV (88.0%), and 18 were low-risk HPV infections (12.0%). The composition of the cervical microbiota were significantly different between the HPV+group and the HPV-group, which in the HPV+group, the α diversity of the cervical microbiota were significantly increased (Shannon index, W=8 174, P<0.000 1; PD whole tree, W=8 887, P=0.001 7). The β diversity of the two groups was significantly different (Binary Jaccard, F=2.325 4, P=0.042 0; Bray Curtis, F=2.136 44, P=0.044 0). The relative abundance of Lactobacillus spp. and L.iners in the HPV+group sample decreased significantly (W=7 730, P<0.000 1; W=8 979, P=0.002 5), accompanied by enriched Achromobacter, Stenotrophomonas, Methylobacterium, Sneathia and Dialister. There was no significant difference in the composition of the cervical microbiota between high-risk HPV infection and low-risk HPV infection ( F=4.100 4, P>0.05). In addition, cervicitis is significantly related to HPV infection (χ2=19.78, P<0.000 1), the composition of cervical flora has similarity features in cervicitis and HPV infection samples. Compared with the normal group, the cervical microbiota of cervicitis with HPV infection is mainly enriched in Achromobacter, Aerococcaceae, Streptococcus, Fusobacteria, and Xanthomonadaceae.Conclusion:The cervical microbiota of patients with HPV infection has a significant dysbiosis, with increased diversity and significant depletion of lactobacillus, accompanied by an increase in the abundance of pathogenic bacteria such as Achromobacter.

Objective:To investigate the characteristics of cervical microbiota in patients with HPV (Human Papillomavirus) infection, and to analyze the associations of cervical microbiota and HPV infection or cervicitis.Methods:300 samples underwent HPV nucleic acid testing was collected in this case-control study from June 2019 to April 2020 in the Zhujiang Hospital of Southern Medical University, there were 150 cases allocated in HPV infection group (HPV+), and 150 cases of negative nucleic acid test were non-infectious Group (HPV-). Next-generation sequencing was used to sequence the V4 region of the bacterial 16S rRNA gene, and QIIME pipeline was used to analysis the microbiota composition of the two groups. Wilcoxon rank sum test and Kruskal-Wallis test were used to statistically analysis the differences of the microbiota between groups; and the α diversity and β diversity of the flora between groups were statistically analyzed by Adonis multivariate analysis of variance and Wilcoxon rank sum test.Results:A total of 300 samples were analyzed in this study, of which 150 samples were HPV-positive and 150 samples were HPV-negative; among HPV-positive cases, 132 were infected by high-risk HPV (88.0%), and 18 were low-risk HPV infections (12.0%). The composition of the cervical microbiota were significantly different between the HPV+group and the HPV-group, which in the HPV+group, the α diversity of the cervical microbiota were significantly increased (Shannon index, W=8 174, P<0.000 1; PD whole tree, W=8 887, P=0.001 7). The β diversity of the two groups was significantly different (Binary Jaccard, F=2.325 4, P=0.042 0; Bray Curtis, F=2.136 44, P=0.044 0). The relative abundance of Lactobacillus spp. and L.iners in the HPV+group sample decreased significantly (W=7 730, P<0.000 1; W=8 979, P=0.002 5), accompanied by enriched Achromobacter, Stenotrophomonas, Methylobacterium, Sneathia and Dialister. There was no significant difference in the composition of the cervical microbiota between high-risk HPV infection and low-risk HPV infection ( F=4.100 4, P>0.05). In addition, cervicitis is significantly related to HPV infection (χ2=19.78, P<0.000 1), the composition of cervical flora has similarity features in cervicitis and HPV infection samples. Compared with the normal group, the cervical microbiota of cervicitis with HPV infection is mainly enriched in Achromobacter, Aerococcaceae, Streptococcus, Fusobacteria, and Xanthomonadaceae.Conclusion:The cervical microbiota of patients with HPV infection has a significant dysbiosis, with increased diversity and significant depletion of lactobacillus, accompanied by an increase in the abundance of pathogenic bacteria such as Achromobacter.