Turning to immunosuppressive tumors: Deciphering the immunosenescence-related microenvironment and prognostic characteristics in pancreatic cancer, in which GLUT1 contributes to gemcitabine resistance.

Increasing evidence indicates that the remodeling of immune microenvironment heterogeneity influences pancreatic cancer development, as well as sensitivity to chemotherapy and immunotherapy. However, a gap remains in the exploration of the immunosenescence microenvironment in pancreatic cancer. In this study, we identified two immunosenescence-associated isoforms (IMSP1 and IMSP2), with consequential differences in prognosis and immune cell infiltration. We constructed the MLIRS score, a hazard score system with robust prognostic performance (area under the curve, AUC = 0.91), based on multiple machine learning algorithms (101 cross-validation methods). Patients in the high MLIRS score group had worse prognosis (P < 0.0001) and lower abundance of immune cell infiltration. Conversely, the low MLIRS score group showed better sensitivity to chemotherapy and immunotherapy. Additionally, our MLIRS system outperformed 68 other published signatures. We identified the immunosenescence microenvironmental windsock GLUT1 with certain co-expression properties with immunosenescence markers. We further demonstrated its positive modulation ability of proliferation, migration, and gemcitabine resistance in pancreatic cancer cells. To conclude, our study focused on training of composite machine learning algorithms in multiple datasets to develop a robust machine learning modeling system based on immunosenescence and to identify an immunosenescence-related microenvironment windsock, providing direction and guidance for clinical prediction and application.

Bioactivity of polysaccharides derived from bivalves.

Bivalves have high diversity, widely distributed in various aquatic environments, including saltwater, brackish water and freshwater. Bivalves are known to rich in polysaccharides and have wide applications in functional foods, pharmaceuticals, and industrial research. Despite many relevant reports are available, the information is poorly organized. Therefore, in this study, we conducted a comprehensive scientific review on the potential bioactivity of polysaccharides derived from bivalves. In general, the polysaccharides derived from bivalves possess various bioactive properties, including anticancer, antioxidant, anticoagulant and immunomodulatory activities. The bioactivity of these biomolecules highly depends on the bivalve species, extraction methods, purification methods, dosages, etc. The information in this study can provide an overview of the bioactivities of bivalve polysaccharides. This is very useful to be used as a guide for identifying the health benefits of polysaccharides derived from different bivalve species.

Marine algal polysaccharides as future potential constituents against non-alcoholic steatohepatitis.

Non-alcoholic steatohepatitis (NASH) is one of the most chronic and incurable liver diseases triggered mainly by an inappropriate diet and hereditary factors which burden liver metabolic stress, and may result in liver fibrosis or even cancer. While the available drugs show adverse side effects. The non-toxic bioactive molecules derived from natural resources, particularly marine algal polysaccharides (MAPs), present significant potential for treating NASH. In this review, we summarized the protective effects of MAPs on NASH from multiple perspectives, including reducing oxidative stress, regulating lipid metabolism, enhancing immune function, preventing fibrosis, and providing cell protection. Furthermore, the mechanisms of MAPs in treating NASH were comprehensively described. Additionally, we highlight the influences of the special structures of MAPs on their bioactive differences. Through this comprehensive review, we aim to further elucidate the molecular mechanisms of MAPs in NASH and inspire insights for deeper research on the functional food and clinical applications of MAPs.

A new approach: Evaluation of necroptosis and immune status enables prediction of the tumor microenvironment and treatment targets in pancreatic cancer.

Growing evidence indicates a potential correlation between necroptosis and pancreatic cancer, and the relationship between necroptosis, immune infiltration and the microenvironment in pancreatic cancer has drawn increasing attention. However, two-dimensional phenotype and prognostic assessment systems based on a combination of necroptosis and immunity have not been explored. In our present study, we explored the pancancer genomics signature of necroptosis-related molecules, identifying necroptosis-related molecule mutation profiles, expression profiles, and correlations between expression levels and methylation/CNV levels. We identified distinct necroptotic as well as immune statuses in pancreatic cancer, and a high necroptosis phenotype and high immunity phenotype both indicated better prognosis than a low necroptosis phenotype and low immunity phenotype. The two-dimensional phenotype we constructed has ideal discriminative effects on pancreatic cancer prognosis, inflammation, and the immune microenvironment. The "high-necroptosis and high-immunity (HNHI)" group exhibited the best prognosis and the highest proportion of infiltrating immune cells. The NI score can be used to predict patient prognosis and is correlated with the immune microenvironment score, chemotherapeutic drug IC50, and tumor mutational burden. In addition, it may be useful for predicting the effect of individualized chemotherapy and immunotherapy. Our study also revealed that SLC2A1 is associated with both necroptosis and immunity and acts as a potential oncogene in pancreatic cancer. In conclusion, the two-dimensional phenotype and NI score we developed are promising tools for clinical multiomics applications and prediction of chemotherapy and immunotherapy response and present benefits in terms of precision medicine and individualized treatment decision-making for pancreatic cancer patients.

Total synthesis of bi-magnolignan.

Bi-magnolignan, isolated from the leaves of Magnolia officinalis, has shown excellent physiological activity against tumor cells. An efficient strategy for the first total synthesis of bi-magnolignan is reported. The bi-dibenzofuran skeleton was constructed via functional group interconversions of commercially available materials 1,2,4-trimethoxybenzene and 4-allylanisole. Then, the dibenzofuran skeleton was afforded by subsequent Suzuki coupling and intramolecular dehydration. The total synthesis of natural product was accomplished through FeCl3 catalyzed oxidative coupling.

Manifestation of the malignant progression of glioma following initial intracerebral hemorrhage: A case report.

BACKGROUND: Intracranial hemorrhage is extremely rare during the initial stages of glioma. Here, we report a case of glioma with unclassified pathology and intracranial bleeding. CASE SUMMARY: After the second surgery for intracerebral hemorrhage, the patient experienced weakness in the left arm and leg, but could walk unassisted. One month after discharge, the weakness in the left limbs had exacerbated and the patient also suffered from headaches and dizziness. A third surgery was ineffective against the rapidly growing tumor. Intracerebral hemorrhage may be the initial symptom of glioma in some rare cases, and atypical perihematomal edema can be used for diagnosis during an emergency. Certain histological and molecular features seen in our case were similar to that of glioblastoma with a primitive neuronal component, which is termed diffuse glioneuronal tumor with features similar to oligodendroglioma and nuclear clusters (DGONC). The patient underwent three surgeries to remove the tumor. The first tumor resection had been performed when the patient was 14-years-old. Resection of the hemorrhage and bone disc decompression were performed when the patient was 39-years-old. One month after the last discharge, the patient underwent neuronavigation-assisted resection of the right frontotemporal parietal lesion plus extended flap decompression. On the 50th d after the third operation, computed tomography imaging showed rapid tumor growth accompanied by brain hernia. The patient was discharged and died 3 d later. CONCLUSION: Glioma can present as bleeding in the initial stage and should be considered in such a setting. We have reported a case of DGONC, which is a rare molecular subtype of glioma with a unique methylation profile.

Turning up the heat on non-immunoreactive tumors: autophagy influences the immune microenvironment in pancreatic cancer.

BACKGROUND: Autophagy regulators play important roles in the occurrence and development of a variety of tumors and are involved in immune regulation and drug resistance. However, the modulatory roles and prognostic value of autophagy regulators in pancreatic cancer have not been identified. METHODS: Transcriptomic data and survival information from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were used to construct a risk score model. Important clinical features were analyzed to generate a nomogram. In addition, we used various algorithms, including ssGSEA, CIBERSORT, XCELL, EPIC, TIMER, and QUANTISEQ, to evaluate the roles of autophagy regulators in the pancreatic cancer immune microenvironment. Furthermore, the mutation landscape was compared between different risk groups. RESULTS: Pan cancer analysis indicated that most of the autophagy regulators were upregulated in pancreatic cancer and were correlated with methylation and CNV level. MET, TSC1, and ITGA6 were identified as the prognostic autophagy regulators and used to construct a risk score model. Some critical clinical indicators, such as age, American Joint Committee on Cancer (AJCC) T stage, AJCC N stage, alcohol and sex, were combined with the risk model to establish the nomogram, which may offer clinical guidance. In addition, our study demonstrated that the low score groups exhibited high immune activity and high abundances of various immune cells, including T cells, B cells, and NK cells. Patients with high risk scores exhibited lower half inhibitory concentration (IC50) values for paclitaxel and had downregulated expression profiles of PD1, CTLA4, and LAG3. Mutation investigation indicated that the high risk groups exhibited a higher mutation burden and higher mutation number compared to the low risk groups. additionally, we verified our risk stratification method using cytology and histology data from our center, and the results are satisfactory. CONCLUSION: We speculated that autophagy regulators have large effects on the prognosis, immune landscape and drug sensitivity of pancreatic cancer. Our model, which combines critical autophagy regulators and clinical indicators, will provide guidance for clinical treatment.

Studies toward synthesis of the core skeleton of spiroaspertrione A.

Bioassay-guided isolation of spiroaspertrione A from cultures of Aspergillus sp. TJ23 in 2017 demonstrated potent resensitization of oxacillin against methicillin-resistant Staphylococcus aureus by lowering the oxacillin minimal inhibitory concentration up to 32-fold. To construct this unique spiro[bicyclo[3.2.2]nonane-2,1'-cyclohexane] system, a protocol for ceric ammonium nitrate-induced intramolecular cross-coupling of silyl enolate is disclosed.

Pyroptosis-related lncRNA pairs to estimate the molecular features and prognostic outcomes of pancreatic ductal adenocarcinoma.

Pyroptosis is a form of programmed cell death associated with inflammatory alterations. However, the intrinsic mechanisms and underlying correlation of pyroptosis-related lncRNAs (PRLs) in pancreatic ductal adenocarcinoma (PDAC) remain unclear. The objective of the current research was to identify pyroptosis-related lncRNAs and a prognostic model to predict the prognosis of patients. We extracted pyroptosis-related lncRNAs to construct a risk model and validated them at Fudan University Shanghai Cancer Center. Crosstalk between lncRNA SNHG10 and GSDMD was found to regulate pyroptosis levels. A new algorithm was used to establish a 0 or 1 PRL pair matrix and prognostic model. Six pyroptosis-related lncRNA pairs were identified and utilized to construct a risk model. The low-risk groups exhibited better prognoses than the high-risk groups. The area under the curve (AUC) indicated extremely high accuracy, reaching 0.810 at 1 year, 0.850 at 2 years, and 0.850 at 3 years in the training set. Patients with different risk scores exhibited distinct metabolic, inflammatory, and immune microenvironments as well as tumor mutation landscapes. Additionally, 9 commonly used chemotherapeutic drugs exhibited different sensitivities between the high- and low-risk groups. To conclude, we propose that pyroptosis exhibits a close correlation with PDAC. Our risk model based on PRL pairs may be beneficial for the accurate estimation of prognostic outcomes, the immune microenvironment, and drug sensitivity, bringing therapeutic hope for patients with PDAC.

SAFNet: A deep spatial attention network with classifier fusion for breast cancer detection.

Breast cancer is a top dangerous killer for women. An accurate early diagnosis of breast cancer is the primary step for treatment. A novel breast cancer detection model called SAFNet is proposed based on ultrasound images and deep learning. We employ a pre-trained ResNet-18 embedded with the spatial attention mechanism as the backbone model. Three randomized network models are trained for prediction in the SAFNet, which are fused by majority voting to produce more accurate results. A public ultrasound image dataset is utilized to evaluate the generalization ability of our SAFNet using 5-fold cross-validation. The simulation experiments reveal that the SAFNet can produce higher classification results compared with four existing breast cancer classification methods. Therefore, our SAFNet is an accurate tool to detect breast cancer that can be applied in clinical diagnosis.

Gracilaria lemaneiformis polysaccharides alleviate colitis by modulating the gut microbiota and intestinal barrier in mice.

Gracilaria lemaneiformis polysaccharide (GLP) has varieties of antioxidation, however, the therapeutic effects of GLP on ulcerative colitis (UC) and the potential mechanisms involved are still incomplete. In the study, the analysis of the ζ-potential, thermal, and morphology properties demonstrated that GLP was a negatively charged polymer, and had great thermostability and irregular network. Moreover, the GLP treatment has the effects of reducing the severity of colitis caused by dextran sulfate sodium by alleviating the colon damage of mice, and increasing the amount of short-chain fatty acids in the intestines, alleviating histopathological inflammation. The sequencing results and α-diversity analysis showed that GLP could improve biodiversity, restore the abundance of Bacteroidetes, and decrease the proportion of Firmicutes. The level of CCL-25 and CCR-9 were inhibited, CD40 and TGF-ß1 were increased. In summary, GLP has potentiality to be utilized as a hopeful functional food to the UC patients.

Construction of a paclitaxel-related competitive endogenous RNA network and identification of a potential regulatory axis in pancreatic cancer.

BACKGROUND: Increasing numbers of studies have elucidated the role of competitive endogenous RNA (ceRNA) networks in carcinogenesis. However, the potential role of the paclitaxel-related ceRNA network in the innate mechanism and prognosis of pancreatic cancer has not been identified. METHODS: Comprehensive bioinformatics analyses were performed to identify drug-related miRNAs (DRmiRNAs), drug-related mRNAs (DRmRNAs) and drug-related lncRNAs (DRlncRNAs) and construct a ceRNA network. The ssGSEA and CIBERSORT algorithms were utilized for immune cell infiltration analysis. Additionally, we validated our paclitaxel-related ceRNA regulatory axis at the gene expression level; functional experiments were conducted to explore the biological functions of the key genes. RESULTS: A total of 182 mRNAs, 13 miRNAs, and 53 lncRNAs were confirmed in the paclitaxel-related ceRNA network. In total, 6 mRNAs, 4 miRNAs, and 6 lncRNAs were identified to establish a risk signature and exhibited optimal prognostic effects. The mRNA signature can predict the abundance of immune cell infiltration and the sensitivity of different chemotherapeutic drugs and may also have a guiding effect in immune checkpoint therapy. A potential PART1/hsa-mir-21/SCRN1 axis was confirmed according to the ceRNA theory and was verified by qPCR. The results indicated that PART1 knockdown markedly increased hsa-mir-21 expression but inhibited SCRN1 expression, weakening the proliferation and migration abilities. CONCLUSIONS: We hypothesized that the paclitaxel-related ceRNA network strongly influences the innate mechanism, prognosis, and immune infiltration of pancreatic cancer. Our risk signatures can accurately predict survival outcomes and provide a clinical basis.

TBNet: a context-aware graph network for tuberculosis diagnosis.

BACKGROUND AND OBJECTIVE: Tuberculosis (TB) is an infectious bacterial disease. It can affect the human lungs, brain, bones, and kidneys. Pulmonary tuberculosis is the most common. This airborne bacterium can be transmitted with the droplets by coughing and sneezing. So far, the most convenient and effective method for diagnosing TB is through medical imaging. Computed tomography (CT) is the first choice for lung imaging in clinics because the conditions of the lungs can be interpreted from CT images. However, manual screening poses an enormous burden for radiologists, resulting in high inter-observer variances. Hence, developing computer-aided diagnosis systems to implement automatic TB diagnosis is an emergent and significant task for researchers and practitioners. This paper proposed a novel context-aware graph neural network called TBNet to detect TB from chest CT images METHODS: Traditional convolutional neural networks can extract high-level image features to achieve good classification performance on the ImageNet dataset. However, we observed that the spatial relationships between the feature vectors are beneficial for the classification because the feature vector may share some common characteristics with its neighboring feature vectors. To utilize this context information for the classification of chest CT images, we proposed to use a feature graph to generate context-aware features. Finally, a context-aware random vector functional-link net served as the classifier of the TBNet to identify these context-aware features as TB or normal RESULTS: The proposed TBNet produced state-of-the-art classification performance for detecting TB from healthy samples in the experiments CONCLUSIONS: Our TBNet can be an accurate and effective verification tool for manual screening in clinical diagnosis.

PBTNet: A New Computer-Aided Diagnosis System for Detecting Primary Brain Tumors.

Brain tumors are among the leading human killers. There are over 120 different types of brain tumors, but they mainly fall into two groups: primary brain tumors and metastatic brain tumors. Primary brain tumors develop from normal brain cells. Early and accurate detection of primary brain tumors is vital for the treatment of this disease. Magnetic resonance imaging is the most common method to diagnose brain diseases, but the manual interpretation of the images suffers from high inter-observer variance. In this paper, we presented a new computer-aided diagnosis system named PBTNet for detecting primary brain tumors in magnetic resonance images. A pre-trained ResNet-18 was selected as the backbone model in our PBTNet, but it was fine-tuned only for feature extraction. Then, three randomized neural networks, Schmidt neural network, random vector functional-link, and extreme learning machine served as the classifiers in the PBTNet, which were trained with the features and their labels. The final predictions of the PBTNet were generated by the ensemble of the outputs from the three classifiers. 5-fold cross-validation was employed to evaluate the classification performance of the PBTNet, and experimental results demonstrated that the proposed PBTNet was an effective tool for the diagnosis of primary brain tumors.

Microorganisms in chemotherapy for pancreatic cancer: An overview of current research and future directions.

Pancreatic cancer is a malignant tumor of the digestive system with a very high mortality rate. While gemcitabine-based chemotherapy is the predominant treatment for terminal pancreatic cancer, its therapeutic effect is not satisfactory. Recently, many studies have found that microorganisms not only play a consequential role in the occurrence and progression of pancreatic cancer but also modulate the effect of chemotherapy to some extent. Moreover, microorganisms may become an important biomarker for predicting pancreatic carcinogenesis and detecting the prognosis of pancreatic cancer. However, the existing experimental literature is not sufficient or convincing. Therefore, further exploration and experiments are imperative to understanding the mechanism underlying the interaction between microorganisms and pancreatic cancer. In this review, we primarily summarize and discuss the influences of oncolytic viruses and bacteria on pancreatic cancer chemotherapy because these are the two types of microorganisms that are most often studied. We focus on some potential methods specific to these two types of microorganisms that can be used to improve the efficacy of chemotherapy in pancreatic cancer therapy.

CGENet: A Deep Graph Model for COVID-19 Detection Based on Chest CT.

Accurate and timely diagnosis of COVID-19 is indispensable to control its spread. This study proposes a novel explainable COVID-19 diagnosis system called CGENet based on graph embedding and an extreme learning machine for chest CT images. We put forward an optimal backbone selection algorithm to select the best backbone for the CGENet based on transfer learning. Then, we introduced graph theory into the ResNet-18 based on the k-nearest neighbors. Finally, an extreme learning machine was trained as the classifier of the CGENet. The proposed CGENet was evaluated on a large publicly-available COVID-19 dataset and produced an average accuracy of 97.78% based on 5-fold cross-validation. In addition, we utilized the Grad-CAM maps to present a visual explanation of the CGENet based on COVID-19 samples. In all, the proposed CGENet can be an effective and efficient tool to assist COVID-19 diagnosis.