Holmemunoz5057

costs in CLI patients. Machine learning and model-based, data-driven medicine may complement physicians' evidence-based medical services. These findings also support the PPPM framework that a paradigm shift from post-diagnosis disease care to early management of comorbidities and targeted prevention is warranted to deliver a cost-effective medical services and desirable healthcare economy. © European Association for Predictive, Preventive and Personalised Medicine (EPMA) 2020.Background Prevention and improvement of disease symptoms are important issues, and probiotics are suggested as a good treatment for controlling the obesity. Human gut microbiota has different community structures. Because gut microbial composition is assumed to be linked to probiotic function, this study evaluated the efficacy of probiotics on obesity-related clinical markers according to gut microbial enterotype. Methods Fifty subjects with body mass index over 25 kg/m2 were randomly assigned to either the probiotic or placebo group. Each group received either unlabeled placebo or probiotic capsules for 12 weeks. Body weight, waist circumference, and body composition were measured every 3 weeks. Using computed tomography, total abdominal fat area and visceral fat area were measured. Blood and fecal samples were collected before and after the intervention for biochemical parameters and gut microbial compositions analysis. Results Gut microbial compositions of all the subjects were classified into two enterotypes according to Prevotella/Bacteroides ratio. The fat percentage, blood glucose, and insulin significantly increased in the Prevotella-rich enterotype of the placebo group. The obesity-related markers, such as waist circumference, total fat area, visceral fat, and ratio of visceral to subcutaneous fat area, were significantly reduced in the probiotic group. The decrease of obesity-related markers was greater in the Prevotella-rich enterotype than in the Bacteroides-rich enterotype. Conclusion Administration of probiotics improved obesity-related markers in obese people, and the efficacy of probiotics differed per gut microbial enterotype and greater responses were observed in the Prevotella-dominant enterotype. © European Association for Predictive, Preventive and Personalised Medicine (EPMA) 2020.Background Cellulite is a common physiological condition of dermis, epidermis, and subcutaneous tissues experienced by 85 to 98% of the post-pubertal females in developed countries. Infrared (IR) thermography combined with artificial intelligence (AI)-based automated image processing can detect both early and advanced cellulite stages and open up the possibility of reliable diagnosis. Although the cellulite lesions may have various levels of severity, the quality of life of every woman, both in the physical and emotional sphere, is always an individual concern and therefore requires patient-oriented approach. Objectives The purpose of this work was to elaborate an objective, fast, and cost-effective method for automatic identification of different stages of cellulite based on IR imaging that may be used for prescreening and personalization of the therapy. Materials and methods In this study, we use custom-developed image preprocessing algorithms to automatically select cellulite regions and combine a total ofpreventive, and personalized medicine (PPPM). © The Author(s) 2020.Background and limitations Impaired wound healing (WH) and chronic inflammation are hallmarks of non-communicable diseases (NCDs). However, despite WH being a recognized player in NCDs, mainstream therapies focus on (un)targeted damping of the inflammatory response, leaving WH largely unaddressed, owing to three main factors. The first is the complexity of the pathway that links inflammation and wound healing; the second is the dual nature, local and systemic, of WH; and the third is the limited acknowledgement of genetic and contingent causes that disrupt physiologic progression of WH. PR-171 order Proposed approach Here, in the frame of Predictive, Preventive, and Personalized Medicine (PPPM), we integrate and revisit current literature to offer a novel systemic view on the cues that can impact on the fate (acute or chronic inflammation) of WH, beyond the compartmentalization of medical disciplines and with the support of advanced computational biology. Conclusions This shall open to a broader understanding of the causes for WH going awry, offering new operational criteria for patients' stratification (prediction and personalization). While this may also offer improved options for targeted prevention, we will envisage new therapeutic strategies to reboot and/or boost WH, to enable its progression across its physiological phases, the first of which is a transient acute inflammatory response versus the chronic low-grade inflammation characteristic of NCDs. © European Association for Predictive, Preventive and Personalised Medicine (EPMA) 2019.Optimization and execution of chemical reactions are to a large extend based on experience and chemical intuition of a chemist. The chemical intuition is rooted in the phenomenological Le Chatelier's principle that teaches us how to shift equilibrium by manipulating the reaction conditions. To access the underlying thermodynamic parameters and their condition-dependencies from the first principles is a challenge. Here, we present a theoretical approach to model non-standard free energies for a complex catalytic CO2 hydrogenation system under operando conditions and identify the condition spaces where catalyst deactivation can potentially be suppressed. Investigation of the non-standard reaction free energy dependencies allows rationalizing the experimentally observed activity patterns and provides a practical approach to optimization of the reaction paths in complex multicomponent reactive catalytic systems. © 2019 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA.Background Bamboo, a lignocellulosic feedstock, is considered as a potentially excellent raw material and evaluated for lignocellulose degradation and bioethanol production, with a focus on using physical and chemical pre-treatment. However, studies reporting the biodegradation of bamboo lignocellulose using microbes such as bacteria and fungi are scarce. Results In the present study, Bacillus velezensis LC1 was isolated from Cyrtotrachelus buqueti, in which the symbiotic bacteria exhibited lignocellulose degradation ability and cellulase activities. We performed genome sequencing of B. velezensis LC1, which has a 3929,782-bp ring chromosome and 46.5% GC content. The total gene length was 3,502,596 bp using gene prediction, and the GC contents were 47.29% and 40.04% in the gene and intergene regions, respectively. The genome contains 4018 coding DNA sequences, and all have been assigned predicted functions. Carbohydrate-active enzyme annotation identified 136 genes annotated to CAZy families, including GH, GTs, CEs, PLs, AAs and CBMs.