Prognostic and clinicopathological value of systemic inflammation response index (SIRI) in patients with breast cancer: a meta-analysis.

BACKGROUND: Many studies have explored the value of the systemic inflammation response index (SIRI) in predicting the prognosis of patients with breast cancer (BC); however, their findings remain controversial. Consequently, we performed the present meta-analysis to accurately identify the role of SIRI in predicting BC prognosis. METHODS: PubMed, Embase, Cochrane Library, and Web of Science databases were comprehensively searched between their inception and February 10, 2024. The significance of SIRI in predicting overall survival (OS) and disease-free survival (DFS) in BC patients was analyzed by calculating pooled hazard ratios (HRs) and corresponding 95% confidence intervals (CIs). RESULTS: Eight articles involving 2,997 patients with BC were enrolled in the present study. According to our combined analysis, a higher SIRI was markedly associated with dismal OS (HR = 2.43, 95%CI = 1.42-4.15, p < 0.001) but not poor DFS (HR = 2.59, 95%CI = 0.81-8.24, p = 0.107) in patients with BC. Moreover, based on the pooled results, a high SIRI was significantly related to T3-T4 stage (OR = 1.73, 95%CI = 1.40-2.14, p < 0.001), N1-N3 stage (OR = 1.61, 95%CI = 1.37-1.91, p < 0.001), TNM stage III (OR = 1.63, 95%CI = 1.34-1.98, p < 0.001), and poor differentiation (OR = 1.25, 95%CI = 1.02-1.52, p = 0.028). CONCLUSION: According to our results, a high SIRI significantly predicted poor OS in patients with BC. Furthermore, elevated SIRI was also remarkably related to increased tumor size and later BC tumor stage. The SIRI can serve as a novel prognostic biomarker for patients with BC.

Based on our knowledge, this study is the first meta-analysis to explore value of SIRI in predicting BC prognosis.According to our results, a high SIRI significantly predicted the dismal OS in BC patients.SIRI can serve as the novel prognostic biomarker for BC patients.

PCF-Net: Position and context information fusion attention convolutional neural network for skin lesion segmentation.

Skin lesion segmentation is a crucial step in the process of skin cancer diagnosis and treatment. The variation in position, shape, size and edges of skin lesion areas poses a challenge for accurate segmentation of skin lesion areas through dermoscopic images. To meet these challenges, in this paper, using UNet as the baseline model, a convolutional neural network based on position and context information fusion attention is proposed, called PCF-Net. A novel two-branch attention mechanism is designed to aggregate Position and Context information, called Position and Context Information Aggregation Attention Module (PCFAM). A global context information complementary module (GCCM) was developed to obtain long-range dependencies. A multi-scale grouped dilated convolution feature extraction module (MSEM) was proposed to capture multi-scale feature information and place it in the bottleneck of UNet. On the ISIC2018 dataset, a large volume of ablation experiments demonstrated the superiority of PCF-Net for dermoscopic image segmentation after adding PCFAM, GCCM and MSEM. Compared with other state-of-the-art methods, the performance of PCF-Net achieves a competitive result in all metrics.

iU-Net: a hybrid structured network with a novel feature fusion approach for medical image segmentation.

In recent years, convolutional neural networks (CNNs) have made great achievements in the field of medical image segmentation, especially full convolutional neural networks based on U-shaped structures and skip connections. However, limited by the inherent limitations of convolution, CNNs-based methods usually exhibit limitations in modeling long-range dependencies and are unable to extract large amounts of global contextual information, which deprives neural networks of the ability to adapt to different visual modalities. In this paper, we propose our own model, which is called iU-Net bacause its structure closely resembles the combination of i and U. iU-Net is a multiple encoder-decoder structure combining Swin Transformer and CNN. We use a hierarchical Swin Transformer structure with shifted windows as the primary encoder and convolution as the secondary encoder to complement the context information extracted by the primary encoder. To sufficiently fuse the feature information extracted from multiple encoders, we design a feature fusion module (W-FFM) based on wave function representation. Besides, a three branch up sampling method(Tri-Upsample) has developed to replace the patch expand in the Swin Transformer, which can effectively avoid the Checkerboard Artifacts caused by the patch expand. On the skin lesion region segmentation task, the segmentation performance of iU-Net is optimal, with Dice and Iou reaching 90.12% and 83.06%, respectively. To verify the generalization of iU-Net, we used the model trained on ISIC2018 dataset to test on PH2 dataset, and achieved 93.80% Dice and 88.74% IoU. On the lung feild segmentation task, the iU-Net achieved optimal results on IoU and Precision, reaching 98.54% and 94.35% respectively. Extensive experiments demonstrate the segmentation performance and generalization ability of iU-Net.

Dermoscopic image segmentation based on Pyramid Residual Attention Module.

We propose a stacked convolutional neural network incorporating a novel and efficient pyramid residual attention (PRA) module for the task of automatic segmentation of dermoscopic images. Precise segmentation is a significant and challenging step for computer-aided diagnosis technology in skin lesion diagnosis and treatment. The proposed PRA has the following characteristics: First, we concentrate on three widely used modules in the PRA. The purpose of the pyramid structure is to extract the feature information of the lesion area at different scales, the residual means is aimed to ensure the efficiency of model training, and the attention mechanism is used to screen effective features maps. Thanks to the PRA, our network can still obtain precise boundary information that distinguishes healthy skin from diseased areas for the blurred lesion areas. Secondly, the proposed PRA can increase the segmentation ability of a single module for lesion regions through efficient stacking. The third, we incorporate the idea of encoder-decoder into the architecture of the overall network. Compared with the traditional networks, we divide the segmentation procedure into three levels and construct the pyramid residual attention network (PRAN). The shallow layer mainly processes spatial information, the middle layer refines both spatial and semantic information, and the deep layer intensively learns semantic information. The basic module of PRAN is PRA, which is enough to ensure the efficiency of the three-layer architecture network. We extensively evaluate our method on ISIC2017 and ISIC2018 datasets. The experimental results demonstrate that PRAN can obtain better segmentation performance comparable to state-of-the-art deep learning models under the same experiment environment conditions.

SwinBTS: A Method for 3D Multimodal Brain Tumor Segmentation Using Swin Transformer.

Brain tumor semantic segmentation is a critical medical image processing work, which aids clinicians in diagnosing patients and determining the extent of lesions. Convolutional neural networks (CNNs) have demonstrated exceptional performance in computer vision tasks in recent years. For 3D medical image tasks, deep convolutional neural networks based on an encoder-decoder structure and skip-connection have been frequently used. However, CNNs have the drawback of being unable to learn global and remote semantic information well. On the other hand, the transformer has recently found success in natural language processing and computer vision as a result of its usage of a self-attention mechanism for global information modeling. For demanding prediction tasks, such as 3D medical picture segmentation, local and global characteristics are critical. We propose SwinBTS, a new 3D medical picture segmentation approach, which combines a transformer, convolutional neural network, and encoder-decoder structure to define the 3D brain tumor semantic segmentation job as a sequence-to-sequence prediction challenge in this research. To extract contextual data, the 3D Swin Transformer is utilized as the network's encoder and decoder, and convolutional operations are employed for upsampling and downsampling. Finally, we achieve segmentation results using an improved Transformer module that we built for increasing detail feature extraction. Extensive experimental results on the BraTS 2019, BraTS 2020, and BraTS 2021 datasets reveal that SwinBTS outperforms state-of-the-art 3D algorithms for brain tumor segmentation on 3D MRI scanned images.

MTPA_Unet: Multi-Scale Transformer-Position Attention Retinal Vessel Segmentation Network Joint Transformer and CNN.

Retinal vessel segmentation is extremely important for risk prediction and treatment of many major diseases. Therefore, accurate segmentation of blood vessel features from retinal images can help assist physicians in diagnosis and treatment. Convolutional neural networks are good at extracting local feature information, but the convolutional block receptive field is limited. Transformer, on the other hand, performs well in modeling long-distance dependencies. Therefore, in this paper, a new network model MTPA_Unet is designed from the perspective of extracting connections between local detailed features and making complements using long-distance dependency information, which is applied to the retinal vessel segmentation task. MTPA_Unet uses multi-resolution image input to enable the network to extract information at different levels. The proposed TPA module not only captures long-distance dependencies, but also focuses on the location information of the vessel pixels to facilitate capillary segmentation. The Transformer is combined with the convolutional neural network in a serial approach, and the original MSA module is replaced by the TPA module to achieve finer segmentation. Finally, the network model is evaluated and analyzed on three recognized retinal image datasets DRIVE, CHASE DB1, and STARE. The evaluation metrics were 0.9718, 0.9762, and 0.9773 for accuracy; 0.8410, 0.8437, and 0.8938 for sensitivity; and 0.8318, 0.8164, and 0.8557 for Dice coefficient. Compared with existing retinal image segmentation methods, the proposed method in this paper achieved better vessel segmentation in all of the publicly available fundus datasets tested performance and results.

Effect of Hcp Iron Ion Regulation on the Interaction Between Acinetobacter baumannii With Human Pulmonary Alveolar Epithelial Cells and Biofilm Formation.

Acinetobacter baumannii is a type of bacterial nosocomial infection with severe drug resistance. Hemolysin co-regulated protein (Hcp) is a marker of activated type VI secretion system (T6SS), a key secretory system that promotes Gram-negative bacteria colonization, adhesion, and invasion of host cells. Hcp is also regulated by iron ions (Fe). In this study, an ATCC17978 hcp deletion strain (ATCC17978Δhcp), an hcp complement strain (ATCC17978Δhcp+ ), and an A. baumannii-green fluorescent protein (GFP) strain were constructed and used to investigate the role of hcp in bacterial adhesion to cells (human pulmonary alveolar epithelial cells (HPAEpiC)) and biofilm formation. Our results indicate that the inhibitory concentrations of the three A. baumannii strains (ATCC17978 wild type, ATCC17978Δhcp, and ATCC17978Δhcp+) were drug-sensitive strains. A. baumannii hcp gene and iron ions might be involved in promoting the formation of a biofilm and host-bacteria interaction. Iron ions affected the ability of A. baumannii to adhere to cells, as there was no significant difference in the bacterial numbers when assessing the adhesion of the three strains to HPAEpiC in the presence of iron ion concentrations of 0 µM (F = 3.1800, p = 0.1144), 25 µM (F = 2.067, p = 0.2075), 100 µM (F = 30.52, p = 0.0007), and 400 µM (F = 17.57, p = 0.0031). The three strains showed significant differences in their ability to adhere to HPAEpiC. The numbers of bacteria adhesion to HPAEpiC were ATCC17978Δhcp>ATCC17978Δhcp+>ATCC17978 in descending order. Hcp gene was positively regulated by iron ions in the bacteria-cells' co-culture. It is speculated that the effect of iron ions on the interaction between A. baumannii and HPAEpiC might be related to the transport function of hcp and bacterial immune escape mechanisms.

Dynamic Monitoring of sTREM-1 and Other Biomarkers in Acute Cholangitis.

BACKGROUND: Sepsis is a common complication of acute cholangitis (AC), which is associated with a high mortality rate. Our study is aimed at exploring the significance of white blood cell (WBC), C-reactive protein (CRP), procalcitonin (PCT), soluble triggering receptor expressed on myeloid cells 1 (sTREM-1), and temperature (T) alone or combined together in early identification and curative effect monitoring of AC with or without sepsis. METHODS: 65 consecutive cases with AC and 76 control cases were enrolled. They were divided into three groups: Group A (AC with sepsis), Group B (AC without sepsis), and Group C (inpatients without AC or other infections). The levels of WBC, CRP, PCT, sTREM-1, and temperature were measured dynamically. The study was carried out and reported according to STARD 2015 reporting guidelines. RESULTS: CRP had the highest AUC to identify AC from individuals without AC or other infections (AUC 1.000, sensitivity 100.0%, specificity 100.0%, positive predictive value 100.0%, and negative predictive value 100.0%). Among various single indexes, PCT performed best (AUC 0.785, sensitivity 75.8%, specificity 72.2%, positive predictive value 68.7%, and negative predictive value 78.8%) to distinguish sepsis with AC, while different combinations of indexes did not perform better. From day 1 to day 5 of hospitalization, the levels of sTREM-1 in Group A were the highest, followed by Groups B and C (P < 0.05); on day 8, sTREM-1 levels in Groups A and B declined back to normal. However, other index levels among three groups still had a significant difference on day 10. Both in Groups A and B, sTREM-1 levels declined fast between day 1 and day 2 (P < 0.05). CONCLUSIONS: CRP is the best biomarker to suggest infection here. PCT alone is sufficient enough to diagnose sepsis with AC. sTREM-1 is the best biomarker to monitor patients' response to antimicrobial therapy and biliary drainage.

Effect of calcium on the interaction of Acinetobacter baumannii with human respiratory epithelial cells.

BACKGROUND: Investigating the factors that influence Acinetobacter baumannii(Ab) adhesion/invasion of host cells is important to understand its pathogenicity. Metal cations have been shown to play an important role in regulating the biofilm formation and increasing the virulence of Ab; however, the effect of calcium on host-bacterial interaction has yet to be clarified. Here, the dynamic process of the interaction between Ab and human respiratory epithelial cells and the effect of calcium on host-bacterial interaction were explored using microscopic imaging, quantitative PCR and real time cellular analysis (RTCA). RESULTS: The concentration of calcium, multiplicity of infection and co-culture time were all demonstrated to have effects on host-bacterial interaction. A unique "double peak" phenomenon changed to a sharp "single peak" phenomenon during the process of Ab infection under the effect of calcium was observed in the time-dependent cell response profiles. Moreover, calcium can increase Ab adhesion/invasion of epithelial cells by regulating the expression of Ab-related genes (ompA, bfmRS, abaI). CONCLUSIONS: Effective control of calcium concentrations can provide new approaches for the prevention and treatment of multi-drug resistant Ab.