Astragalus polysaccharide ameliorates CD8+ T cell dysfunction through STAT3/Gal-3/LAG3 pathway in inflammation-induced colorectal cancer.

Chronic inflammation can promote cancer development as observed in inflammation-induced colorectal cancer (CRC). However, the poor treatment outcomes emphasize the need for effective treatment. Astragalus polysaccharide (APS), a vital component of the natural drug Astragalus, has anti-tumor effects by inhibiting cancer cell proliferation and enhancing immune function. In this study, we found that APS effectively suppressed CRC development through activating CD8+ T cells and reversing its inhibitory state in the tumor microenvironment (TME) of AOM/DSS inflammation-induced CRC mice. Network pharmacology and clinical databases suggested that the STAT3/ Galectin-3(Gal-3)/LAG3 pathway might be APS's potential target for treating CRC and associated with CD8+ T cell dysfunction. In vivo experiments showed that APS significantly reduced phosphorylated STAT3 and Gal-3 levels in tumor cells, as well as LAG3 in CD8+ T cells. Co-culture experiments with MC38 and CD8+ T cells demonstrated that APS decreased the expression of co-inhibitory receptor LAG3 in CD8+ T cells by targeting STAT3/Gal-3 in MC38 cells. Mechanism investigations revealed that APS specifically improved CD8+ T cell function through modulation of the STAT3/Gal-3/LAG3 pathway to inhibit CRC development, providing insights for future clinical development of natural anti-tumor drugs and immunotherapies as a novel strategy combined with immune checkpoint inhibitors (ICIs).

Gut microbiota-derived 12-ketolithocholic acid suppresses the IL-17A secretion from colonic group 3 innate lymphoid cells to prevent the acute exacerbation of ulcerative colitis.

Intestinal microbiota dysbiosis and metabolic disruption are well-known as the primary triggers of ulcerative colitis (UC). However, their role in regulating the group 3 innate lymphoid cells (ILC3s), which are essential for intestinal health, remains unexplored during the development of disease severity. Here, our results showed that the microbiota structure of patients with severe UC (SUCs) differed from those with mild UC (MiUCs), moderate UC (MoUCs), and healthy controls (HCs). Microbes producing secondary bile acids (SBAs) and SBAs decreased with the aggravation of UC, and a strong positive correlation existed between them. Next, fecal microbiota transfer was used to reproduce the human-derived microbiota in mice and decipher the microbiota-mediated inflammatory modulation during an increase in disease severity. Mice receiving SUC-derived microbiota exhibited enhancive inflammation, a lowered percentage of ILC3s, and the down-regulated expressions of bile acid receptors, including vitamin D receptor (VDR) and pregnane X receptor (PXR), in the colon. Similar to clinical results, SBA-producing microbes, deoxycholic acids (DCA), and 12-ketolithocholic acids (12-KLCA) were diminished in the intestine of these recipients. Finally, we compared the therapeutic potential of DCA and 12-KLCA in preventing colitis and the regulatory mechanisms mediated by ILC3s. 12-KLCA but not DCA represented a strong anti-inflammatory effect associated with the higher expression of VDR and the lower secretion of IL-17A from colonic ILC3s. Collectively, these findings provide new signatures for monitoring the acute deterioration of UC by targeting gut microbiota and bile acid metabolism and demonstrate the therapeutic and preventive potential of a novel microbiota-derived metabolite, 12-KLCA.

Bifidobacterium animalis subsp. lactis BL-99 ameliorates colitis-related lung injury in mice by modulating short-chain fatty acid production and inflammatory monocytes/macrophages.

Pulmonary inflammation as one of the extraintestinal manifestations of ulcerative colitis (UC) has attracted extensive attention, and its pathogenesis is closely related to gut dysbiosis. Bifidobacterium animalis subsp. lactis BL-99 (BL-99) can alleviate osteoporosis caused by UC, but less research has been done on other extraintestinal manifestations (EIM) caused by UC. This study aimed to explore the role and potential mechanisms of BL-99 on DSS-induced pulmonary complications in colitis mice. The results showed that BL-99 decreased weight loss, disease activity index score, colonic pathology score, and the production of pro-inflammatory cytokines (e.g., TNF-α, IL-1ß, and IL-6) in colitis mice. BL-99 also alleviated DSS-induced lung pathological damage by suppressing the infiltration of pro-inflammatory cytokines, inflammatory monocytes, and macrophages. Furthermore, 16S rRNA gene sequencing showed lower abundances of several potentially pathogenic bacteria (e.g., Burkholderia, Shigella, and Clostridium perfringens) and enrichment in specific beneficial bacteria (e.g., Adlercreutzia and Bifidobacterium animalis) in colitis mice with BL-99 treatment. Targeted metabolomics suggested that BL-99 intervention promoted the production of intestinal acetate and butyrate. Finally, we observed that the pulmonary expression of primary acetate and butyrate receptors, including FFAR2, FFAR3, and, GPR109a, was up-regulated in BL-99-treated mice, which negatively correlated with inflammatory monocytes and macrophages. Altogether, these results suggest that BL-99 might be utilized as a probiotic intervention to prevent the incidence of colitis-related lung injury owing to its ability to shape the intestinal microbiota and suppress inflammation.

IL-17A aggravates asthma-induced intestinal immune injury by promoting neutrophil trafficking.

With the concept of the gut-lung axis reinforced in recent years, emerging evidence has shown that intestinal homeostasis is vital for lung health. Nevertheless, the impacts of lung homeostasis on intestinal tracts and their mechanism are rarely studied. Our results showed that papain-induced asthmatic mice exhibited apparent colonic injuries compared with controls, including increased intestinal permeability, neutrophil and Th17 infiltration in the colonic lamina propria. Moreover, the intranasal administration of papain aggravated such colonic injuries in mice with dextran sulfate sodium-induced colitis, as evidenced by increased occult blood scores, shortened colon length, and accumulated neutrophils. The level of IL-17A was also higher in the serum of asthmatic mice than wild-type mice. Interestingly, the pathologic scores, the proportion of Th17 cells, and neutrophil infiltration in the colon were markedly reduced after IL-17A blocking. Similarly, longer length, lower pathologic scores, and fewer neutrophils were also observed in the colon of IL-17-deficient asthmatic mice. More importantly, we demonstrated that severe gastrointestinal symptoms could accompany clinical asthmatics. The frequencies of Th17 cells and the mRNA expression of IL-17A in the peripheral blood of these patients were significantly enhanced. Besides, the gastrointestinal symptom rating scale scores positively correlated with the frequencies of Th17 in asthmatics. These findings enlighten that IL-17A aggravates asthma-induced intestinal immune injury by promoting neutrophil trafficking, which facilitates the exploration of new potential biomarkers to treat asthma.

SHA-MTL: soft and hard attention multi-task learning for automated breast cancer ultrasound image segmentation and classification.

Purpose The automatic analysis of ultrasound images facilitates the diagnosis of breast cancer effectively and objectively. However, due to the characteristics of ultrasound images, it is still a challenging task to achieve analyzation automatically. We suppose that the algorithm will extract lesion regions and distinguish categories easily if it is guided to focus on the lesion regions.Method We propose a multi-task learning (SHA-MTL) model based on soft and hard attention mechanisms for breast ultrasound (BUS) image simultaneous segmentation and binary classification. The SHA-MTL model consists of a dense CNN encoder and an upsampling decoder, which are connected by attention-gated (AG) units with soft attention mechanism. Cross-validation experiments are performed on BUS datasets with category and mask labels, and multiple comprehensive analyses are performed on the two tasks.Results We assess the SHA-MTL model on a public BUS image dataset. For the segmentation task, the sensitivity and DICE of the SHA-MTL model to the lesion regions increased by 2.27% and 1.19% compared with the single task model, respectively. The classification accuracy and F1 score increased by 2.45% and 3.82%, respectively.Conclusion The results validate the effectiveness of our model and indicate that the SHA-MTL model requires less a priori knowledge to achieve better results by comparing with other recent models. Therefore, we can draw the conclusion that paying more attention to the lesion region of BUS is conducive to the discrimination of lesion types.

Traditional Chinese Medicine in Cancer Care: An Overview of 5834 Randomized Controlled Trials Published in Chinese.

BACKGROUND: Traditional Chinese medicine (TCM) is widely integrated into cancer care in China. An overview in 2011 identified 2384 randomized and non-randomized controlled trials (RCTs, non-RCTs) on TCM for cancer published in the Chinese literature. This article summarizes updated evidence of RCTs on TCM for cancer care. METHODS: We searched 4 main Chinese databases: China National Knowledge Infrastructure, Chinese Scientific Journal Database, SinoMed, and Wanfang. RCTs on TCM used in cancer care were analyzed in this bibliometric study. RESULTS: Of 5834 RCTs (477 157 cancer patients), only 62 RCTs were indexed in MEDLINE. The top 3 cancers treated were lung, stomach, and breast cancer. About 4752 RCTs (81.45%) tested TCM combined with conventional treatment, and 1082 RCTs (18.55%) used TCM alone for treating symptoms and side-effects. Herbal medicine was the most frequently used TCM modality (5087 RCTs; 87.20%). The most frequently reported outcome was symptom improvement (3712 RCTs; 63.63%) followed by quality of life (2725 RCTs; 46.71%), and biomarkers (2384 RCTs; 40.86%). The majority of RCTs (4051; 69.44%) concluded there were beneficial effects using either TCM alone or TCM plus conventional treatment compared with conventional treatment. CONCLUSION: Substantial randomized trials demonstrated different types/stages of cancer were treated by various TCM modalities, alone or in combination with conventional medicine. Further evaluation on the effects and safety of TCM modalities focusing on outcomes such as quality of life is required.

Accurate Screening of COVID-19 Using Attention-Based Deep 3D Multiple Instance Learning.

Automated Screening of COVID-19 from chest CT is of emergency and importance during the outbreak of SARS-CoV-2 worldwide in 2020. However, accurate screening of COVID-19 is still a massive challenge due to the spatial complexity of 3D volumes, the labeling difficulty of infection areas, and the slight discrepancy between COVID-19 and other viral pneumonia in chest CT. While a few pioneering works have made significant progress, they are either demanding manual annotations of infection areas or lack of interpretability. In this paper, we report our attempt towards achieving highly accurate and interpretable screening of COVID-19 from chest CT with weak labels. We propose an attention-based deep 3D multiple instance learning (AD3D-MIL) where a patient-level label is assigned to a 3D chest CT that is viewed as a bag of instances. AD3D-MIL can semantically generate deep 3D instances following the possible infection area. AD3D-MIL further applies an attention-based pooling approach to 3D instances to provide insight into each instance's contribution to the bag label. AD3D-MIL finally learns Bernoulli distributions of the bag-level labels for more accessible learning. We collected 460 chest CT examples: 230 CT examples from 79 patients with COVID-19, 100 CT examples from 100 patients with common pneumonia, and 130 CT examples from 130 people without pneumonia. A series of empirical studies show that our algorithm achieves an overall accuracy of 97.9%, AUC of 99.0%, and Cohen kappa score of 95.7%. These advantages endow our algorithm as an efficient assisted tool in the screening of COVID-19.

Direct estimation of left ventricular ejection fraction via a cardiac cycle feature learning architecture.

The left ventricular ejection fraction is of significant importance for the early identification and diagnosis of cardiac disease. However, estimation of the left ventricular ejection fraction with consistently reliable and high accuracy remains a great challenge, owing to the high variability of cardiac structures and the complexity of the temporal dynamics in the cardiac magnetic resonance imaging sequences. The popular methods of left ventricular ejection fraction estimation rely on the left ventricular volume. Thus, strong prior knowledge is often necessary, impeding the ease of use of the existing methods as clinical tools. In this study, we propose a cardiac cycle feature learning architecture for achieving an accurate and reliable estimation of the left ventricular ejection fraction. The proposed method constructs a cardiac cycle extraction module that generates and analyzes an optical flow to obtain the cardiac cycle of all images, a motion feature fusion and extraction module for temporal modeling of the cardiac sequences, and a fully connected regression module for achieving a direct estimation. Experiments on 2900 left ventricle segments of 145 subjects from short-axis magnetic resonance imaging sequences of multiple lengths prove that our proposed method achieves reliable performance (correlation coefficient: 0.946; mean absolute error 2.67; standard deviation: 3.23). As compared with the current state-of-the-art method, our proposed method improves the performance by approximately 3% insofar as the mean absolute error. As the first solution for estimating the left ventricular ejection fraction directly, our proposed method demonstrates great potential for future clinical applications.