Intestinal microbiota: a new perspective on delaying aging?

The global aging situation is severe, and the medical pressures associated with aging issues should not be underestimated. The need and feasibility of studying aging and intervening in aging have been confirmed. Aging is a complex natural physiological progression, which involves the irreversible deterioration of body cells, tissues, and organs with age, leading to enhanced risk of disease and ultimately death. The intestinal microbiota has a significant role in sustaining host dynamic balance, and the study of bidirectional communication networks such as the brain–gut axis provides important directions for human disease research. Moreover, the intestinal microbiota is intimately linked to aging. This review describes the intestinal microbiota changes in human aging and analyzes the causal controversy between gut microbiota changes and aging, which are believed to be mutually causal, mutually reinforcing, and inextricably linked. Finally, from an anti-aging perspective, this study summarizes how to achieve delayed aging by targeting the intestinal microbiota. Accordingly, the study aims to provide guidance for further research on the intestinal microbiota and aging

Convolutional Neural Networks for Classification of T2DM Cognitive Impairment Based on Whole Brain Structural Features

PurposeCognitive impairment is generally found in individuals with type 2 diabetes mellitus (T2DM). Although they may not have visible symptoms of cognitive impairment in the early stages of the disorder, they are considered to be at high risk. Therefore, the classification of these patients is important for preventing the progression of cognitive impairment.MethodsIn this study, a convolutional neural network was used to construct a model for classifying 107 T2DM patients with and without cognitive impairment based on T1-weighted structural MRI. The Montreal cognitive assessment score served as an index of the cognitive status of the patients.ResultsThe classifier could identify T2DM-related cognitive decline with a classification accuracy of 84.85% and achieved an area under the curve of 92.65%.ConclusionsThe model can help clinicians analyze and predict cognitive impairment in patients and enable early treatment

Extracting Nested Biomedical Entity Relations by Tagging Dependency Chains

Biomedical event extraction is an important research topic in the field of biomedical text mining. However, much research work is required before event extraction systems become applicable. Thus, we proposed a novel and efficient approach for extracting nested biomedical events. First, using dependency parsing, we extracted the target sequences that contained biomedical entity (trigger/argument) chains. Second, the Condition Random Fields (CRFs) model was used to tag the entity chains which represented the nested argument-trigger edges. Thirdly, the post-processing step was used to output the events. This method is a new attempt to treat the biomedical event extraction as a sequence tagging problem. The experiment results showed that we got the performance of 47.3 in F-score which is promising when compared with the joint ML-based system in BioNLP-ST2013. Furthermore, we estimated the results of the trigger detection, which outperformed the state-of–the-art systems on the same corpus. Therefore, our work is a positive contribution to the biomedical text mining community

Biotechnological advances for improving natural pigment production: a state-of-the-art review

In current years, natural pigments are facing a fast-growing global market due to the increase of people’s awareness of health and the discovery of novel pharmacological effects of various natural pigments, e.g., carotenoids, flavonoids, and curcuminoids. However, the traditional production approaches are source-dependent and generally subject to the low contents of target pigment compounds. In order to scale-up industrial production, many efforts have been devoted to increasing pigment production from natural producers, via development of both in vitro plant cell/tissue culture systems, as well as optimization of microbial cultivation approaches. Moreover, synthetic biology has opened the door for heterologous biosynthesis of pigments via design and re-construction of novel biological modules as well as biological systems in bio-platforms. In this review, the innovative methods and strategies for optimization and engineering of both native and heterologous producers of natural pigments are comprehensively summarized. Current progress in the production of several representative high-value natural pigments is also presented; and the remaining challenges and future perspectives are discussed.Published versionThis work was funded by the National Natural Science Foundation of China (22108097), Natural Science Foundation of Jiangsu Province (BK20200616), National Key Research and Development Program of China (Grant No. 2018YFA0901800), China Postdoctoral Science Foundation (2020M671339), and Zhejiang Provincial Natural Science Foundation of China (Grant No. LZ20B060002)

Antimicrobial and antioxidant activities of phenolic metabolites from flavonoid-producing yeast : potential as natural food preservatives

We analysed the antimicrobial and antioxidant activities of phenolic metabolites secreted from a naringenin-producing Saccharomyces cerevisiae strain (a GRAS organism), against the pure flavonoid naringenin and its prenylated derivatives, to assess their potential as natural food preservatives. Agar disc diffusion assay was used to analyse the antimicrobial activity against Escherichia coli ATCC 25922 and Staphylococcus aureus ATCC 29213, while DMPD chemiluminescence assay was used to analyse antioxidant activity, based on DMPD+-scavenging activity. Our results showed that the engineered yeast metabolites exhibited both strong antimicrobial and DMPD+-scavenging activity, particularly the metabolite phenylacetaldehyde. Pure naringenin had poor antimicrobial and DMPD+-scavenging effects. Prenylated varieties, 6-prenylnaringenin and 8-prenylnaringenin, inhibited only S. aureus, while only 8-prenylnaringenin exhibited moderate DMPD+-scavenging activity. Our results suggested that phenolic metabolites secreted from naringenin-producing yeast would be a sustainable source of natural food preservatives

Metabolic engineering of Saccharomyces cerevisiae for de novo production of kaempferol

Kaempferol is a polyphenolic compound with various reported health benefits and thus harbors considerable potential for food-engineering applications. In this study, a high-yield kaempferol-producing cell factory was constructed by multiple strategies, including gene screening, elimination of the phenylethanol biosynthetic branch, optimizing the core flavonoid synthetic pathway, supplementation of precursor PEP/E4P, and mitochondrial engineering of F3H and FLS. A total of 86 mg/L of kaempferol was achieved in strain YL-4, to date the highest production titer in yeast. Furthermore, a coculture system and supplementation of surfactants were investigated, to relieve the metabolic burden as well as the low solubility/possible transport limitations of flavonoids, respectively. In the coculture system, the whole pathway was divided across two strains, resulting in 50% increased cell growth. Meanwhile, supplementation of Tween 80 in our engineered strains yielded 220 mg/L of naringenin and 200 mg/L of mixed flavonoids—among the highest production titer reported via de novo production in yeast.Nanyang Technological UniversityThis work was supported by Nanyang Technological University, Singapore (iFood Research grant)

Atrial Fibrillation Detection Based on a Residual CNN Using BCG Signals

Atrial fibrillation (AF) is the most common arrhythmia and can seriously threaten patient health. Research on AF detection carries important clinical significance. This manuscript proposes an AF detection method based on ballistocardiogram (BCG) signals collected by a noncontact sensor. We first constructed a BCG signal dataset consisting of 28,214 ten-second nonoverlapping segments collected from 45 inpatients during overnight sleep, including 9438 for AF, 9570 for sinus rhythm (SR), and 9206 for motion artifacts (MA). Then, we designed a residual convolutional neural network (CNN) for AF detection. The network has four modules, namely a downsampling convolutional module, a local feature learning module, a global feature learning module, and a classification module, and it extracts local and global features from BCG signals for AF detection. The model achieved precision, sensitivity, specificity, F1 score, and accuracy of 96.8%, 93.7%, 98.4%, 95.2%, and 96.8%, respectively. The results indicate that the AF detection method proposed in this manuscript could serve as a basis for long-term screening of AF at home based on BCG signal acquisition

Yeast-derived plant phenolic emulsions as novel, natural, and sustainable food preservatives

Food preservatives are ubiquitously used to increase the shelf life of manufactured foods. Current synthetic preservatives offer negligible health benefits, while healthier alternatives such as botanical extracts are hindered by unsustainable production means. Here, we developed a natural, functional food preservative via a combination of sustainable food technologies: food-grade yeast engineered to produce natural plant compounds combined with emulsifiers valorized from milk whey and fruit pectin. Chemical compositional analysis of the yeast phenolic extract (N2) revealed naringenin, phloretin, phloretic acid, and p-coumaric acid as the key plant phenolic compounds present, with negligible traces of biomolecular contamination. N2 demonstrated improved antibacterial and antifungal activities compared to those of commercial preservatives in antimicrobial assays. When used in tandem with whey protein-pectin conjugate emulgents, N2 successfully extended the shelf life of fresh apple juice. Our results pave the way toward health-promoting preservatives as well as real food applications for similarly sustainably engineered food ingredients.Nanyang Technological UniversityThis work was supported by a FoodTech@NTU Grant from Nanyang Technological University