Application of FGD-BCEL loss function in segmenting temporal lobes on localized CT images for radiotherapy

ObjectivesThe aim of this study was to find a new loss function to automatically segment temporal lobes on localized CT images for radiotherapy with more accuracy and a solution to dealing with the classification of class-imbalanced samples in temporal lobe segmentation.MethodsLocalized CT images for radiotherapy of 70 patients with nasopharyngeal carcinoma were selected. Radiation oncologists sketched mask maps. The dataset was randomly divided into the training set (n = 49), the validation set (n = 7), and the test set (n = 14). The training set was expanded by rotation, flipping, zooming, and shearing, and the models were evaluated using Dice similarity coefficient (DSC), Jaccard similarity coefficient (JSC), positive predictive value (PPV), sensitivity (SE), and Hausdorff distance (HD). This study presented an improved loss function, focal generalized Dice-binary cross-entropy loss (FGD-BCEL), and compared it with four other loss functions, Dice loss (DL), generalized Dice loss (GDL), Tversky loss (TL), and focal Tversky loss (FTL), using the U-Net model framework.ResultsWith the U-Net model based on FGD-BCEL, the DSC, JSC, PPV, SE, and HD were 0.87 ± 0.11, 0.78 ± 0.11, 0.90 ± 0.10, 0.87 ± 0.13, and 4.11 ± 0.75, respectively. Except for the SE, all the other evaluation metric values of the temporal lobes segmented by the FGD-BCEL-based U-Net model were improved compared to the DL, GDL, TL, and FTL loss function-based U-Net models. Moreover, the FGD-BCEL-based U-Net model was morphologically more similar to the mask maps. The over- and under-segmentation was lessened, and it effectively segmented the tiny structures in the upper and lower poles of the temporal lobe with a limited number of samples.ConclusionsFor the segmentation of the temporal lobe on localized CT images for radiotherapy, the U-Net model based on the FGD-BCEL can meet the basic clinical requirements and effectively reduce the over- and under-segmentation compared with the U-Net models based on the other four loss functions. However, there still exists some over- and under-segmentation in the results, and further improvement is needed

Individual and combined associations of alanine aminotransferase and hemoglobin with metabolic syndrome in the elderly in Qingdao, China

Background and aimsCombined associations of alanine aminotransferase (ALT) and hemoglobin (Hb) with metabolic syndrome (MetS) have not been assessed yet. The current study investigated the independent and combined relationships between ALT, Hb, and MetS in the elderly.MethodsThe 37,966 elderly participants aged 65 years and older were recruited from community centers in Qingdao, China. The sampled elderly population visited the health centers once a year where they were offered a free health checkup. Based on a combination of ALT and Hb levels categorized by tertile, participants were grouped into nine groups (Group 1–9). Logistic regression models were used to analyze the individual and combined associations of ALT and Hb with MetS.ResultsALT and Hb were both independently related to MetS in both genders. With the elevation of ALT or Hb levels, risks for MetS and its components increased. Compared to the reference group (the 1st tertiles of both ALT and Hb levels), respective odds ratio of combined ALT and Hb for MetS in Group 2–9 ranged from 1.32–3.38 and 1.14–2.31 in men and women after adjusting for age, sex, education, married status, current smoking, current drinking, physical activity, and diet habit.ConclusionALT and Hb were both independently related to MetS and its components. Combined ALT and Hb levels could increase risks of MetS and its components than an elevation in ALT or Hb alone

New insights into bacterial mechanisms and potential intestinal epithelial cell therapeutic targets of inflammatory bowel disease

The global incidence of inflammatory bowel disease (IBD) has increased rapidly in recent years, but its exact etiology remains unclear. In the past decade, IBD has been reported to be associated with dysbiosis of gut microbiota. Although not yet proven to be a cause or consequence of IBD, the common hypothesis is that at least some alterations in the microbiome are protective or pathogenic. Furthermore, intestinal epithelial cells (IECs) serve as a protective physical barrier for gut microbiota, essential for maintaining intestinal homeostasis and actively contributes to the mucosal immune system. Thus, dysregulation within the intestinal epithelium increases intestinal permeability, promotes the entry of bacteria, toxins, and macromolecules, and disrupts intestinal immune homeostasis, all of which are associated with the clinical course of IBD. This article presents a selective overview of recent studies on bacterial mechanisms that may be protective or promotive of IBD in biological models. Moreover, we summarize and discuss the recent discovery of key modulators and signaling pathways in the IECs that could serve as potential IBD therapeutic targets. Understanding the role of the IECs in the pathogenesis of IBD may help improve the understanding of the inflammatory process and the identification of potential therapeutic targets to help ameliorate this increasingly common disease

MicroAST: Towards Super-Fast Ultra-Resolution Arbitrary Style Transfer

Arbitrary style transfer (AST) transfers arbitrary artistic styles onto content images. Despite the recent rapid progress, existing AST methods are either incapable or too slow to run at ultra-resolutions (e.g., 4K) with limited resources, which heavily hinders their further applications. In this paper, we tackle this dilemma by learning a straightforward and lightweight model, dubbed MicroAST. The key insight is to completely abandon the use of cumbersome pre-trained Deep Convolutional Neural Networks (e.g., VGG) at inference. Instead, we design two micro encoders (content and style encoders) and one micro decoder for style transfer. The content encoder aims at extracting the main structure of the content image. The style encoder, coupled with a modulator, encodes the style image into learnable dual-modulation signals that modulate both intermediate features and convolutional filters of the decoder, thus injecting more sophisticated and flexible style signals to guide the stylizations. In addition, to boost the ability of the style encoder to extract more distinct and representative style signals, we also introduce a new style signal contrastive loss in our model. Compared to the state of the art, our MicroAST not only produces visually superior results but also is 5-73 times smaller and 6-18 times faster, for the first time enabling super-fast (about 0.5 seconds) AST at 4K ultra-resolutions. Code is available at https://github.com/EndyWon/MicroAST.Comment: Accepted by AAAI 202

Generative Image Inpainting with Segmentation Confusion Adversarial Training and Contrastive Learning

This paper presents a new adversarial training framework for image inpainting with segmentation confusion adversarial training (SCAT) and contrastive learning. SCAT plays an adversarial game between an inpainting generator and a segmentation network, which provides pixel-level local training signals and can adapt to images with free-form holes. By combining SCAT with standard global adversarial training, the new adversarial training framework exhibits the following three advantages simultaneously: (1) the global consistency of the repaired image, (2) the local fine texture details of the repaired image, and (3) the flexibility of handling images with free-form holes. Moreover, we propose the textural and semantic contrastive learning losses to stabilize and improve our inpainting model's training by exploiting the feature representation space of the discriminator, in which the inpainting images are pulled closer to the ground truth images but pushed farther from the corrupted images. The proposed contrastive losses better guide the repaired images to move from the corrupted image data points to the real image data points in the feature representation space, resulting in more realistic completed images. We conduct extensive experiments on two benchmark datasets, demonstrating our model's effectiveness and superiority both qualitatively and quantitatively.Comment: Accepted to AAAI2023, Ora

Targeting Trop2 in solid tumors: a look into structures and novel epitopes

Trophoblast cell surface antigen 2 (Trop2) exhibits limited expression in normal tissues but is over-expressed across various solid tumors. The effectiveness of anti-Trop2 antibody-drug conjugate (ADC) in managing breast cancer validates Trop2 as a promising therapeutic target for cancer treatment. However, excessive toxicity and a low response rate of ADCs pose ongoing challenges. Safer and more effective strategies should be developed for Trop2-positive cancers. The dynamic structural attributes and the oligomeric assembly of Trop2 present formidable obstacles to the progression of innovative targeted therapeutics. In this review, we summarize recent advancements in understanding Trop2’s structure and provide an overview of the epitope characteristics of Trop2-targeted agents. Furthermore, we discuss the correlation between anti-Trop2 agents’ epitopes and their respective functions, particularly emphasizing their efficacy and specificity in targeted therapies