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Dietary fat interacts with receptors in both the oral cavity and the gastrointestinal (GI) tract to regulate fat and energy intake. This review discusses recent developments in our understanding of the mechanisms underlying the effects of fat, through its digestive products, fatty acids (FAs), on GI function and energy intake, the role of oral and intestinal FA receptors, and the implications that changes in oral and small intestinal sensitivity in response to ingested fat may have for the development of obesity.

Factors underlying disturbed appetite perception in anorexia nervosa (AN) are poorly characterized. We examined in patients with AN whether fasting and postprandial appetite perceptions, gastrointestinal (GI) hormones, GI symptoms and state anxiety (i) differed from healthy controls (HCs) and (ii) were modified by two weeks of refeeding. 22 female adolescent inpatients with restricting AN, studied on hospital admission once medically stable (Wk0), and after one (Wk1) and two (Wk2) weeks of high-calorie refeeding, were compared with 17 age-matched HCs. After a 4 h fast, appetite perceptions, GI symptoms, state anxiety, and plasma acyl-ghrelin, cholecystokinin (CCK), peptide tyrosine tyrosine (PYY) and pancreatic polypeptide (PP) concentrations were assessed at baseline and in response to a mixed-nutrient test-meal (479 kcal). Compared with HCs, in patients with AN at Wk0, baseline ghrelin, PYY, fullness, bloating and anxiety were higher, and hunger less, and in response to the meal, ghrelin, bloating and anxiety were greater, and hunger less (all p < 0.05). After two weeks of refeeding, there was no change in baseline or postprandial ghrelin or bloating, or postprandial anxiety, but baseline PYY, fullness and anxiety decreased, and baseline and postprandial hunger increased (p < 0.05). We conclude that in AN, refeeding for 2 weeks was associated with improvements in PYY, appetite and baseline anxiety, while increased ghrelin, bloating and postprandial anxiety persisted.

Previous fMRI studies have demonstrated that glucose decreases the hypothalamic BOLD response in humans. However, the mechanisms underlying the CNS response to glucose have not been defined. We recently demonstrated that the slowing of gastric emptying by glucose is dependent on activation of the gut peptide cholecystokinin (CCK1) receptor. Using physiological functional magnetic resonance imaging this study aimed to determine the whole brain response to glucose, and whether CCK plays a central role. Changes in blood oxygenation level-dependent (BOLD) signal were monitored using fMRI in 12 healthy subjects following intragastric infusion (250ml) of: 1M glucose+predosing with dexloxiglumide (CCK1 receptor antagonist), 1M glucose+placebo, or 0.9% saline (control)+placebo, in a single-blind, randomised fashion. Gallbladder volume, blood glucose, insulin, and GLP-1 and CCK concentrations were determined. Hunger, fullness and nausea scores were also recorded. Intragastric glucose elevated plasma glucose, insulin, and GLP-1, and reduced gall bladder volume (an in vivo assay for CCK secretion). Glucose decreased BOLD signal, relative to saline, in the brainstem and hypothalamus as well as the cerebellum, right occipital cortex, putamen and thalamus. The timing of the BOLD signal decrease was negatively correlated with the rise in blood glucose and insulin levels. The glucose+dex arm highlighted a CCK1-receptor dependent increase in BOLD signal only in the motor cortex. Glucose induces site-specific differences in BOLD response in the human brain; the brainstem and hypothalamus show a CCK1 receptor-independent reduction which is likely to be mediated by a circulatory effect of glucose and insulin, whereas the motor cortex shows an early dexloxiglumide-reversible increase in signal, suggesting a CCK1 receptor-dependent neural pathway. •We have identified two distinct CNS responses to glucose in man.•A CCK1 receptor (CCK1R)-dependent BOLD signal increase in the motor cortex.•A CCK1R-independent BOLD signal decrease in the brainstem and hypothalamus.•The BOLD signal decrease was mediated by changes in blood glucose and insulin,

Objective Gut-derived humoural factors activate central nervous system (CNS) mechanisms controlling energy intake and expenditure, and autonomic outflow. Ghrelin is secreted from the stomach and stimulates food intake and gastric emptying, but the relevant mechanisms are poorly understood. Nutrient-activated CNS systems can be studied in humans by physiological/pharmacological MRI (phMRI). This method has been used to examine the CNS responses to exogenous ghrelin. Design phMRI was used to study the CNS responses in healthy people to a ghrelin bolus (0.3 nmol/kg, intravenous) in the post-prandial state, and an intravenous infusion of ghrelin (1.25 pmol/kg/min) alone and after intragastric lipid (dodecanoate, C12) in people who have fasted. Results A ghrelin bolus decreased the blood oxygenation level dependent (BOLD) signal detected by phMRI in feeding-activated areas of the CNS in the post-prandial state. Infusion of ghrelin reversed the effect of C12 in delaying gastric emptying but had no effect on hunger. Intragastric C12 caused strong bilateral activation of a matrix of CNS areas, including the brain stem, hypothalamus and limbic areas which was attenuated by exogenous ghrelin. Ghrelin infusion alone had a small but significant stimulatory effect on CNS BOLD signals. Conclusion Ghrelin inhibits activation of the hypothalamus and brain stem induced by ingested nutrients, suggesting a role in suppression of gut-derived satiety signals in humans.

Gastric emptying, as well as intragastric meal distribution, and gastrointestinal hormones, including cholecystokinin (CCK), play an important role in appetite regulation. The evaluation of gastrointestinal factors regulating food intake is commonly performed in healthy, lean, young male participants. It has, however, been suggested that there is a marked interindividual variability in the effects of nutrient ‘preloads’ on energy intake in this group. Whether there is significant intraindividual variation in acute energy intake after a nutrient preload, and, if so, how this relates to day-to-day differences in gastric emptying and gastrointestinal hormone release, is unclear. The purpose of the present paper is to evaluate the hypothesis that energy intake after a nutrient preload would be reproducible and associated with reproducible patterns of gastric emptying, intragastric distribution and gastrointestinal hormone release. Fifteen healthy men (age 25 (sem 5) years) consumed a glucose preload (50 g glucose in 300 ml water; 815 kJ) on three occasions. Gastric emptying and intragastric meal distribution (using three-dimensional ultrasound), blood glucose, plasma insulin and CCK concentrations and appetite perceptions were evaluated over 90 min, and energy intake from a cold buffet-style meal was then quantified. Energy intake was highly reproducible within individuals between visits (intraclass correlation coefficient, ri = 0·9). Gastric emptying, intragastric meal distribution, blood glucose, plasma insulin and CCK concentrations and appetite perceptions did not differ between visits (ri>0·7 for all). In healthy males, energy intake is highly reproducible, at least in the short term, and is associated with reproducible patterns of gastric emptying, glycaemia, insulinaemia and CCK release.

Optimisation of tissue engineering (TE) processes requires models that can identify relationships between the parameters to be optimised and predict structural and performance outcomes from both physical and chemical processes. Currently, Design of Experiments (DoE) methods are commonly used for optimisation purposes in addition to playing an important role in statistical quality control and systematic randomisation for experiment planning. DoE is only used for the analysis and optimisation of quantitative data (i.e., number-based, countable or measurable), while it lacks the suitability for imaging and high dimensional data analysis. Machine learning (ML) offers considerable potential for data analysis, providing a greater flexibility in terms of data that can be used for optimisation and predictions. Its application within the fields of biomaterials and TE has recently been explored. This review presents the different types of DoE methodologies and the appropriate methods that have been used in TE applications. Next, ML algorithms that are widely used for optimisation and predictions are introduced and their advantages and disadvantages are presented. The use of different ML algorithms for TE applications is reviewed, with a particular focus on their use in optimising 3D bioprinting processes for tissue-engineered construct fabrication. Finally, the review discusses the future perspectives and presents the possibility of integrating DoE and ML in one system that would provide opportunities for researchers to achieve greater improvements in the TE field.

Cartilage is an avascular tissue with extremely limited self-regeneration capabilities. At present, there are no existing treatments that effectively stop the deterioration of cartilage or reverse its effects; current treatments merely relieve its symptoms and surgical intervention is required when the condition aggravates. Thus, cartilage damage remains an ongoing challenge in orthopaedics with an urgent need for improved treatment options. In recent years, major advances have been made in the development of three-dimensional (3D) bioprinted constructs for cartilage repair applications. 3D bioprinting is an evolutionary additive manufacturing technique that enables the precisely controlled deposition of a combination of biomaterials, cells, and bioactive molecules, collectively known as bioink, layer-by-layer to produce constructs that simulate the structure and function of native cartilage tissue. This review provides an insight into the current developments in 3D bioprinting for cartilage tissue engineering. The bioink and construct properties required for successful application in cartilage repair applications are highlighted. Furthermore, the potential for translation of 3D bioprinted constructs to the clinic is discussed. Overall, 3D bioprinting demonstrates great potential as a novel technique for the fabrication of tissue engineered constructs for cartilage regeneration, with distinct advantages over conventional techniques.

There is a need for effective therapy for COVID-19 pneumonia. Convalescent plasma has antiviral activity and early observational studies suggested benefit in reducing COVID-19 severity. We investigated the safety and efficacy of convalescent plasma in hospitalized patients with COVID-19 in a population with a high HIV prevalence and where few therapeutic options were available. We performed a double-blinded, multicenter, randomized controlled trial in one private and three public sector hospitals in South Africa. Adult participants with COVID-19 pneumonia requiring non-invasive oxygen were randomized 1:1 to receive a single transfusion of 200 mL of either convalescent plasma or 0.9% saline solution. The primary outcome measure was hospital discharge and/or improvement of ≥ 2 points on the World Health Organisation Blueprint Ordinal Scale for Clinical Improvement by day 28 of enrolment. The trial was stopped early for futility by the Data and Safety Monitoring Board. 103 participants, including 21 HIV positive individuals, were randomized at the time of premature trial termination: 52 in the convalescent plasma and 51 in the placebo group. The primary outcome occurred in 31 participants in the convalescent plasma group and and 32 participants in the placebo group (relative risk 1.03 (95% CI 0.77 to 1.38). Two grade 1 transfusion-related adverse events occurred. Participants who improved clinically received convalescent plasma with a higher median anti-SARS-CoV-2 neutralizing antibody titre compared with those who did not (298 versus 205 AU/mL). Our study contributes additional evidence for recommendations against the use of convalescent plasma for COVID-19 pneumonia. Safety and feasibility in this population supports future investigation for other indications.

Platelets mediate arterial thrombosis, a leading cause of myocardial infarction and stroke. During injury, platelets adhere and spread over exposed subendothelial matrix substrates of the damaged blood vessel wall. The mechanisms which govern platelet activation and their interaction with a range of substrates are therefore regularly investigated using platelet spreading assays. These assays often use differential interference contrast (DIC) microscopy to assess platelet morphology and analysis performed using manual annotation. Here, a convolutional neural network (CNN) allowed fully automated analysis of platelet spreading assays captured by DIC microscopy. The CNN was trained using 120 generalised training images. Increasing the number of training images increases the mean average precision of the CNN. The CNN performance was compared to six manual annotators. Significant variation was observed between annotators, highlighting bias when manual analysis is performed. The CNN effectively analysed platelet morphology when platelets spread over a range of substrates (CRP-XL, vWF and fibrinogen), in the presence and absence of inhibitors (dasatinib, ibrutinib and PRT-060318) and agonist (thrombin), with results consistent in quantifying spread platelet area which is comparable to published literature. The application of a CNN enables, for the first time, automated analysis of platelet spreading assays captured by DIC microscopy.