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旨在为进一步提高共轭亚油酸产量提供理论依据，总结概述了共轭亚油酸的生理活性与制备方法的研究进展。共轭亚油酸是一种天然存在于反刍动物体内的物质，具有降低胆固醇含量、抗癌、抗氧化、提高免疫力等重要生理功能，在减肥、癌症治疗、哮喘治疗等方面发挥了重要作用，在功能性膳食研发领域拥有巨大潜力。由于共轭亚油酸的天然含量与来源无法满足人们日常生活的需求，因此研究更高产率的共轭亚油酸的合成方法一直是热点内容。合成共轭亚油酸的方法主要有生物合成法和化学合成法，生物合成法中的微生物合成法和酶催化异构化合成法是近年来的研究热点。 The research progress on the physiological activity and preparation methods of conjugated linoleic acid were summarized and outlined to provide a theoretical basis for further improving the yield of conjugated linoleic acid. Conjugated linoleic acid is a substance naturally existing in ruminants and has important physiological functions such as reducing the contents of cholesterol in the body, anti-cancer, antioxidant and immune enhancement, and plays an important role in weight loss, cancer treatment and asthma disease treatment, and has great potential in the field of functional diet research and development. Since the natural content and source of conjugated linoleic acid can not meet the needs of people′s daily life, the research on the synthesis of conjugated linoleic acid with higher yield has been a hot topic. The main methods for the synthesis of conjugated linoleic acid includ biological synthesis and chemical synthesis. Among biological synthesis, microbial synthesis and enzymatic isomerization synthesis are the research focuses in recent years.

Coronavirus disease 2019 (COVID-19), the global pandemic caused by SARS-CoV-2, has resulted thus far in greater than 933,000 deaths worldwide; yet disease pathogenesis remains unclear. Clinical and immunological features of patients with COVID-19 have highlighted a potential role for changes in immune activity in regulating disease severity. However, little is known about the responses in human lung tissue, the primary site of infection. Here we show that pathways related to neutrophil activation and pulmonary fibrosis are among the major up-regulated transcriptional signatures in lung tissue obtained from patients who died of COVID-19 in Wuhan, China. Strikingly, the viral burden was low in all samples, which suggests that the patient deaths may be related to the host response rather than an active fulminant infection. Examination of the colonic transcriptome of these patients suggested that SARS-CoV-2 impacted host responses even at a site with no obvious pathogenesis. Further proteomics analysis validated our transcriptome findings and identified several key proteins, such as the SARS-CoV-2 entry-associated protease cathepsins B and L and the inflammatory response modulator S100A8/A9, that are highly expressed in fatal cases, revealing potential drug targets for COVID-19.

PurposeTumor genomic features have been of particular interest because of their potential impact on the tumor immune microenvironment and response to immunotherapy. Due to the substantial heterogeneity, an integrative approach incorporating diverse molecular features is needed to characterize immunologic features underlying primary resistance to immunotherapy and for the establishment of novel predictive biomarkers.Experimental designWe developed a pan-cancer deep machine learning model integrating tumor mutation burden, microsatellite instability, and somatic copy-number alterations to classify tumors of different types into different genomic clusters, and assessed the immune microenvironment in each genomic cluster and the association of each genomic cluster with response to immunotherapy.ResultsOur model grouped 8,646 tumors of 29 cancer types from The Cancer Genome Atlas into four genomic clusters. Analysis of RNA-sequencing data revealed distinct immune microenvironment in tumors of each genomic class. Furthermore, applying this model to tumors from two melanoma immunotherapy clinical cohorts demonstrated that patients with melanoma of different genomic classes achieved different benefit from immunotherapy. Interestingly, tumors in cluster 4 demonstrated a cold immune microenvironment and lack of benefit from immunotherapy despite high microsatellite instability burden.ConclusionsOur study provides a proof for principle that deep learning modeling may have the potential to discover intrinsic statistical cross-modality correlations of multifactorial input data to dissect the molecular mechanisms underlying primary resistance to immunotherapy, which likely involves multiple factors from both the tumor and host at different molecular levels.

Aims: To investigate the association of methylation risk score (MRS) and its interactions with environmental factors with type 2 diabetes mellitus (T2DM) risk. Methods: We conducted a nested case–control study with 241 onset cases and 241 matched controls. Conditional logistic regression models were employed to identify risk CpG sites. Simple and weighted MRSs were constructed based on the methylation levels of ATP‐binding cassette G1 gene, fat mass and obesity associated gene, potassium voltage‐gated channel member 1 gene, and thioredoxin‐interacting protein gene previously associated with T2DM to estimate the association of MRS with T2DM risk. Stratified analyses were used to investigate interactions between MRS and environmental factors. Results: A total of 10 CpG loci were identified from the aforementioned genes to calculate MRS. After controlling for potential confounding factors, taking tertile 1 as reference, the odds ratios (ORs) and 95% confidence intervals (CIs) for T2DM of tertile 3 was 2.39 (1.36–4.20) for simple MRS and 2.59 (1.45–4.63) for weighted MRS. With per SD score increment in MRS, the OR (95% CI) was 1.66 (1.29–2.14) and 1.60 (1.24–2.08) for simple and weighted MRSs, respectively. J‐curved associations were observed between both simple and weighted MRSs and T2DM risks. Additionally, multiplication interactions for smoking and hypertension with simple MRS on the risk of T2DM were found, similarly for smoking and obesity with weighted MRS on the risk of T2DM (all Pinteraction <.05). Conclusion: Elevated simple and weighted MRSs were associated with increased risk of T2DM. Environmental risk factors may influence the association between MRS and T2DM. [ABSTRACT FROM AUTHOR]

Background: The objective of this study was to develop and validate a nomogram to predict 1‐year overall survival (OS) and 2‐year OS in patients with high‐grade digestive neuroendocrine neoplasms (NENs) as well as to guide selection of subgroups that could benefit from systemic chemotherapy. Subjects, Materials, and Methods: We performed a retrospective analysis of 223 patients with NENs of the gut and hepato‐biliary‐pancreatic system from four centers included in the development cohort. The nomogram was externally validated in a cohort of 90 patients from another one. Results: The final model included lactate dehydrogenase, performance status, stage, Ki67, and site of primary tumor, all of which had a significant effect on OS. The uncorrected C‐index was 0.761 for OS, and the bias‐corrected C‐index was 0.744. Predictions correlated well with observed 1‐year and 2‐year outcomes (judged by eye). The area under the time‐dependent receiver operating characteristic curve at 12 months and 24 months was 0.876 and 0.838, respectively. The nomogram performed well in terms of both discrimination and calibration when applied to the validation cohort, and OS was significantly different between the two groups classified by nomogram score (log‐rank p <.001). Conclusion: The validated nomogram provided useful prediction of OS, which can be offered for clinicians to improve their abilities to assess patient prognosis, to create clinical risk groups for informing treatment or for patient stratification by disease severity in clinical trials. Implications for Practice: The high‐grade neuroendocrine neoplasms of the digestive system are rare malignancies with great heterogeneity. An overall survival nomogram was developed and externally validated in this study. Two subgroups were classified by the nomogram score, and platinum‐based chemotherapy may not bring clinical benefit for the low‐risk patients. This study aimed to develop and validate a nomogram for prediction of overall survival for patients with high‐grade neuroendocrine neoplasms of the digestive system. [ABSTRACT FROM AUTHOR]