The tumor microenvironment (TME) is a complex ecosystem, which includes many different types of cells, abnormal vascular systems, and immunosuppressive cytokines. TME serves an important function in tumor tolerance and escapes from immune surveillance leading to tumor progression. Indeed, there is increasing evidence that gut microbiome is associated with cancer in a variety of ways, as specific microbial signatures are known to promote cancer development and influence safety, tolerability, and efficacy of therapies. Studies over the past five years have shown that the composition of the intestinal microbiota has a significant impact on the efficacy of anticancer immunosurveillance, which contribute to the therapeutic activity of cancer immunotherapies based on targeting cytotoxic T lymphocyte protein 4 (CTLA-4) or programmed cell death protein 1 (PD-1)–programmed cell death 1 ligand 1 (PD-L1) axis. In this review, we mainly discuss the impact of TME on cancer and immunotherapy through immune-related mechanisms. We subsequently discuss the influence of gut microbiota and its metabolites on the host immune system and the formation of TME. In addition, this review also summarizes the latest research on the role of gut microbiota in cancer immunotherapy.

With the high prevalence of breast cancer, it is urgent to find out the intrinsic difference between various subtypes, so as to infer the underlying mechanisms. Given the available multi-omics data, their proper integration can improve the accuracy of breast cancer subtype recognition. In this study, DeepMO, a model using deep neural networks based on multi-omics data, was employed for classifying breast cancer subtypes. Three types of omics data including mRNA data, DNA methylation data, and copy number variation (CNV) data were collected from The Cancer Genome Atlas (TCGA). After data preprocessing and feature selection, each type of omics data was input into the deep neural network, which consists of an encoding subnetwork and a classification subnetwork. The results of DeepMO based on multi-omics on binary classification are better than other methods in terms of accuracy and area under the curve (AUC). Moreover, compared with other methods using single omics data and multi-omics data, DeepMO also had a higher prediction accuracy on multi-classification. We also validated the effect of feature selection on DeepMO. Finally, we analyzed the enrichment gene ontology (GO) terms and biological pathways of these significant genes, which were discovered during the feature selection process. We believe that the proposed model is useful for multi-omics data analysis.

Background COVID-19 patients develop hypolipidemia. However, it is unknown whether lipid levels have improved and there are potential sequlae in recovered patients. Objective In this follow-up study, we evaluated serum lipidemia and various physiopathological laboratory values in recovered patients. Methods A 3–6 month follow-up study was performed between June 15 and September 3, 2020, to examine serum levels of laboratory values in 107 discharged COVID-19 patients (mild = 59; severe/critical = 48; diagnoses on admission). Sixty-one patients had a revisit chest CT scan. A Wilcoxon signed-rank test was used to analyze changes in laboratory values at admission and follow-up. Results LDL-c and HDL-c levels were significantly higher at follow-up than at admission in severe/critical cases (p <  0.05). LDL-c levels were significantly higher at follow-up than at admission in mild cases (p <  0.05). Coagulation and liver functional values were significantly improved at follow-up than at admission for patients (p <  0.05). Increases in HDL-c significantly correlated with increases in numbers of white blood cells (p <  0.001) during patients’ recovery. With exclusion of the subjects taking traditional Chinese medicines or cholesterol-lowering drugs, LDL-c and HDL-c levels were significantly increased at follow-up than at admission in severe/critical cases (p <  0.05). Residue lesions were observed in CT images in 72% (44 of 61) of follow-up patients. Conclusions Improvements of LDL-c, HDL-c, liver functions, and incomplete resolution of lung lesions were observed at 3–6 month follow-up for recovered patients, indicating that a long-term recovery process could be required and the development of sequelae such as pulmonary fibrosis could be expected in some patients.

Herein, we report a biomimetic oxidative coupling cyclization strategy for the highly efficient functionalization of tetrahydrocarbolines (THCs). This process enables rapid access to complex isochromanoindolenine scaffolds in moderate to excellent yields. The reaction proceeds smoothly and rapidly (complete within minutes) in an open flask. This operationally simple protocol is scalable and compatible with a wide range of functional groups. Late-stage functionalization of a pharmacologically relevant molecule is also demonstrated.

We describe iron-catalyzed oxidative coupling cyclization of tetrahydrocarbazoles or THbCs or THgCs to form benzofuroindolenines as fused polycyclic indoles. This mild, efficient and simple approach afforded a library of more than 52 complex compounds across a range of substrate classes with good to excellent yields.

Background: Autism spectrum disorder (ASD) is characterised by many of features including problem in social interactions, different ways of learning, some children showing a keen interest in specific subjects, inclination to routines, challenges in typical communication, and particular ways of processing sensory information. Early intervention and suitable supports for these children may make a significant contribution to their development. However, considerable difficulties have been encountered in the screening and diagnosis of ASD. The literature has indicated that certain retinal features are significantly associated with ASD. In this study, we investigated the use of machine learning approaches on retinal images to further enhance the classification accuracy. Methods: Forty-six ASD participants were recruited from three special needs schools and 24 normal control were recruited from the community. Among them, 23 age-gender matched ASD and normal control participant-pairs were constructed for the primary analysis. All retinal images were captured using a nonmydriatic fundus camera. Automatic retinal image analysis (ARIA) methodology applying machine-learning technology was used to optimise the information of the retina to develop a classification model for ASD. The model's validity was then assessed using a 10-fold cross-validation approach to assess its validity. Findings: The sensitivity and specificity were 95.7% (95% CI 76.0%, 99.8%) and 91.3% (95% CI 70.5%, 98.5%) respectively. The area under the ROC curve was 0.974 (95% CI 0.934, 1.000); however, it was noted that the specificity for female participants might not be as high as that for male participants. Interpretation: Because ARIA is a fully automatic cloud-based algorithm and relies only on retinal images, it can be used as a risk assessment tool for ASD screening. Further diagnosis and confirmation can then be made by professionals, and potential treatment may be provided at a relatively early stage.

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Staphylococcus aureus (S. aureus) is a common pathogen that causes various serious diseases, including chronic infections. Discovering new antibacterial agents is an important aspect of the pharmaceutical field because of the lack of effective antibacterial drugs. In our research, we found that one anti-S. aureus substance is actinomycin D, originating from Streptomyces parvulus (S. parvulus); then, we further focused on the anti-S. aureus ability and the omics profile of S. aureus in response to actinomycin D. The results revealed that actinomycin D had a significant inhibitory activity on S. aureus with a minimum inhibitory concentration (MIC) of 2 μg/mL and a minimum bactericidal concentration (MBC) of 64 μg/mL. Bacterial reactive oxygen species (ROS) increased 3.5-fold upon treatment with actinomycin D, as was measured with the oxidation-sensitive fluorescent probe DCFH-DA, and H2O2 increased 3.5 times with treatment by actinomycin D. Proteomics and metabolomics, respectively, identified differentially expressed proteins in control and treatment groups, and the co-mapped correlation network of proteomics and metabolomics annotated five major pathways that were potentially related to disrupting the energy metabolism and oxidative stress of S. aureus. All findings contributed to providing new insight into the mechanisms of the anti-S. aureus effects of actinomycin D originating from S. parvulus.