The gut-liver axis denotes the intricate connection and interaction between gut microbiome and liver, in which compositional and functional shifts in gut microbiome affect host metabolism. Hepatic portal vein of the blood circulation system has been thought to be the major route for metabolite transportation in the gut-liver axis, but the existence and importance of other routes remain elusive. Here, we perform metabolome comparison in blood circulation and mesenteric lymph systems and identify significantly shifted metabolites in serum and mesentery. Using cellular assays, we find that the majority of decreased metabolites in lymph system under high-fat diet are effective in alleviating metabolic disorders, indicating a high potential of lymph system in regulating liver metabolism. Among those, a representative metabolite, L-carnitine, reduces diet-induced obesity in mice. Metabolic tracing analysis identifies that L-carnitine is independently transported by the mesenteric lymph system, serving as an example that lymph circulation comprises a second route in the gut-liver axis to modulate liver metabolism. Our study provides new insights into metabolite transportation via mesenteric lymph system in the gut-liver axis, offers an extended scope for the investigations in host-gut microbiota metabolic interactions and potentially new targets in the treatment of metabolic disorders.

Dietary fiber (DF) is one type of carbohydrate that cannot be digested by the gastrointestinal tract. It is widely recognized as an essential ingredient for health due to its remarkable prebiotic properties. Studies have shown that DF is important in the management of metabolic diseases, such as obesity and diabetes, by regulating the balance of gut microbiota and slowing down the absorption of glucose. It is worth noting that patients with metabolic diseases might suffer from intestinal dysfunction (such as constipation), which is triggered by factors such as the disease itself or medication. This increases the complexity of chronic disease treatment. Although medications are the most common treatment for chronic disease, long-term use might increase the financial and psychological burden. DF as a prebiotic has received significant attention not only in the therapy for constipation but also as an adjunctive treatment in metabolic disease. This review focuses on the application of DF in modulating metabolic diseases with special attention on the effect of DF on intestinal dysfunction. Furthermore, the molecular mechanisms through which DF alleviates intestinal disorders are discussed, including modulating the secretion of gastrointestinal neurotransmitters and hormones, the expression of aquaporins, and the production of short-chain fatty acids.

Functional constipation is a gastrointestinal disorder that affects millions of people and is correlated with gut microbiome dysbiosis. The currently available treatments are ineffective; therefore, novel treatment schemes targeting the gut microbiome are desired. The aim of this study was to assess the effects of yogurt supplemented with seven probiotic strains and six types of dietary fibers on functional constipation. In the mouse study, mice with induced constipation were administered the yogurt once a day for 1 wk, with fecal parameters and intestinal transit rate measured. In the clinical study, participants with constipation (N = 86) were given the yogurt once daily (200 g) for 4 wk. Fecal and blood samples along with Patient Assessment of Constipation-symptoms and Patient Assessment of Constipation-Quality of Life Scale questionnaires were collected to evaluate the safety and efficacy of the yogurt. Shotgun metagenomic sequencing was performed to analyze fecal samples of both mice and humans. We found that constipated mice had different gut microbiomes compared with those in healthy controls; yogurt treatment significantly relieved constipation-related symptoms and resulted in shifts in the microbiome. Yogurt also relieved symptoms of antibiotic-induced constipation in mice and restored the gut microbiome to a certain extent. In the clinical trial with 86 patients, yogurt administration significantly improved constipation symptoms and showed no serious adverse effects (was generally considered safe). However, subsequent metagenomic profiling of the gut microbiome did not reveal significant changes in the microbial composition, in contrast to the results in mice. We hypothesize that the differences in dosage between mice and humans may attribute to such discrepancies, and microbiome changes may not be necessary for improvements of constipation symptoms in humans. Results from this study showed that yogurt can potentially be used for the treatment of constipation.

In order to develop bistatic SAR ocean investigation, the first issue is the study of bistatic SAR ocean surface imaging mechanism. And velocity bunching mechanism is a SAR inherent modulation. Thus, the intensity expression in the bistatic SAR image plane is derived in this paper. The expression describes quantificationally the displacement of the scatter elements in the image plane and a degradation in radar resolution in azimuthal direction. In addition, a bistatic SAR linear velocity bunching transfer function is obtained in the linear parameter regime. Finally, this paper analyzes qualitatively the effects that various platform observation conditions have on the bistatic SAR linear imaging range of wave spectrum.

Background: Previously, we found that healthy underweight (HU) subjects, with BMI < 18.5, eat about 12% less food (by calories) each day. It is presently unclear whether this lower intake is associated with them making food choices that provide high satiation and satiety. Methods: Using 7-day photographic records of food intake, we analyzed 52 HU and 50 normal-weight participants. Results: We included 52 HU and 50 normal-weight participants in the final analysis. HU individuals ate 25% fewer calories than normal-weight individuals. Their intake included a higher % of rice (p = 0.0013) and vegetables (p = 0.0006) and a lower % of livestock meat (p = 0.0007), poultry meat (p < 0.0001), and starchy roots (p = 0.0015), compared with the normal-weight population. The percent energy from carbohydrates was significantly higher (p = 0.0234), and the % energy from fat was significantly lower (p < 0.0001) in the HU group, with no difference in the % energy from protein. HU individuals sourced more of their protein from plants. Dietary patterns were grouped into three clusters, with 24 individuals grouped into cluster 1 (87.5% normal-weight population), 28 individuals into cluster 2 (64.3% normal-weight group), and 50 individuals into cluster 3 (78% HU group). Conclusions: The HU group ate less overall and had proportionally more rice and vegetables and less poultry and livestock meat, starchy roots, and drinks. With respect to macronutrients, they also ate a greater % carbohydrates and less % fat, and they sourced more of their protein intake from plant sources. HU individuals did not follow a low-carbohydrate lifestyle.

Background/Objectives: Dietary microstructure affects energy intake. Traditional Universal Eating Monitors (UEMs) offer accuracy but are limited for monitoring diverse diets. We developed the ‘Feeding Table’, a novel UEM that simultaneously tracks intake of up to 12 foods, enabling high-resolution monitoring of eating microstructure for multiple foods simultaneously. Methods: Forty-nine healthy volunteers participated: 15 (10 male, 8 female) in a location preference experiment and 31 (15 male, 16 female) in a standard meal test. The location preference study involved four weekly sessions. Participants received a standardized breakfast based on individual energy needs; lunch intake was measured 3 h later with food items in pseudo-randomized positions. The standard meal test occurred over two consecutive days to assess the Feeding Table’s performance in monitoring eating behavior under standardized conditions. Results: In two consecutive days of standard meal tests, the Feeding Table showed reasonable day-to-day repeatability for energy and macronutrient intake (energy: r = 0.82; fat: r = 0.86; carbohydrate: r = 0.86; protein: r = 0.58). Among the four repeated intake measurements, the results demonstrated high intra-class correlation coefficients (ICCs: energy 0.94, protein 0.90, fat 0.90, and carbohydrate 0.93). No significant positional bias was observed (energy: p = 0.07; macronutrients: p = 0.70). Conclusions: The Feeding Table maintains UEM accuracy while enabling multi-food, real-time monitoring of dietary microstructure and food choice, offering enhanced precision for studying eating behaviors.