Influence of Cementation on the Aesthetical Appearance of Full-Ceramic Restorations.

The use of dental ceramics as restorative materials requires corresponding luting materials (cements) that, in turn, influence the visual appearance of the restoration. Due to the high light transmission through the ceramics, the cements can affect the color perception of the dental restoration. This study aims to investigate the optical effects of various cements on the visual appearance of full-ceramic restorations. Three fixing polymer resins (Bifix SE (VOCO GmbH, Cuxhafen, Germany), BreezeTM (Pentron Clinical, West Collins Orange, CA, USA), and PanaviaTM F. 2.0 (Kuraray, Noritake, Osaka, Japan)), with layer thicknesses of 50, 100, 200, and 250 µm, were applied onto a ceramic base model (0.4 mm thick), and irradiated with laser light of wavelengths 532, 632.8, and 1064 nm. Light intensities and scattering effects of light of various wavelengths were angle-dependent, analyzed using a goniophotometer with perpendicular light incidence on the sample specimen (base model plus luting material). In addition, the transmitted power of the light through the sample specimen was determined as a function of the layer thickness. With increasing layer thickness, power losses of respectively 30% for Bifix SE and BreezeTM in the visible spectral range were comparable, whereas PanaviaTM F. 2.0 showed a power loss of ca. 44% here. For the near-infrared range, the power losses for all cements were 25%. This could be confirmed by the interpretation of the line widths. Moreover, the line widths for thin cement layer thicknesses (50 and 100 µm) in the visible spectral range displayed only a redistribution of light by scattering, which does not affect color perception at all. In addition, at 200 and 250 µm, absorption occurred which causes a change in color perception. Within the scope of this study, it could be shown that for thin-layer thicknesses of the cement applied here, there is no adverse optical effect on the aesthetic visual appearance of the restoration.

Algorithm-Based Hearing and Speech Therapy Rehabilitation after Cochlear Implantation.

INTRODUCTION: Due to the changes in the indication range for cochlear implants and the demographic development towards an aging society, more and more people are in receipt of cochlear implants. An implantation requires a close-meshed audiological and logopedic aftercare. Hearing therapy rehabilitation currently requires great personnel effort and is time consuming. Hearing and speech therapy rehabilitation can be supported by digital hearing training programs. However, the apps currently on the market are to a limited degree personalized and structured. Increasing digitalization makes it possible, especially in times of pandemics, to decouple hearing therapy treatment from everyday clinical practice. MATERIAL AND METHODS: For this purpose, an app is in development that provides hearing therapy tailored to the patient. The individual factors that influence hearing outcome are considered. Using intelligent algorithms, the app determines the selection of exercises, the level of difficulty and the speed at which the difficulty is increased. RESULTS: The app works autonomously without being connected to local speech therapists. In addition, the app is able to analyze patient difficulties within the exercises and provides conclusions about the need for technical adjustments. CONCLUSIONS: The presented newly developed app represents a possibility to support, replace, expand and improve the classic outpatient hearing and speech therapy after CI implantation. The way the application works allows it to reach more people and provide a time- and cost-saving alternative to traditional therapy.

Learning from terrorist mass casualty incidents: a global survey.

BACKGROUND: Reports published directly after terrorist mass casualty incidents frequently fail to capture difficulties that may have been encountered. An anonymised consensus-based platform may enable discussion and collaboration on the challenges faced. Our aim was to identify where to focus improvement for future responses. METHODS: We conducted a mixed methods study by email of clinicians' experiences of leading during terrorist mass casualty incidents. An initial survey identified features that worked well, or failed to, during terrorist mass casualty incidents plus ongoing challenges and changes that were implemented as a result. A follow-up, quantitative survey measured agreement between responses within each of the themes using a Likert scale. RESULTS: Thirty-three participants responded from 22 hospitals that had received casualties from a terrorist incident, representing 17 cities in low-middle, middle and high income countries. The first survey identified themes of sufficient (sometimes abundant) human resource, although coordination of staff was a challenge. Difficulties highlighted were communication, security, and management of blast injuries. The most frequently implemented changes were education on specific injuries, revising future plans and preparatory exercises. Persisting challenges were lack of time allocated to training and psychological well-being. The follow-up survey recorded highest agreement amongst correspondents on the need for re-triage at hospital (90% agreement), coordination roles (85% agreement), flexibility (100% agreement), and large-scale exercises (95% agreement). CONCLUSION: This survey collates international experience gained from clinicians managing terrorist mass casualty incidents. The organisation of human response, rather than consumption of physical supplies, emerged as the main finding. NHSH Clinical Effectiveness Unit project registration number: 2020/21-036.

Pitfalls in HLA Ligandomics-How to Catch a Li(e)gand.

Knowledge about the peptide repertoire presented by human leukocyte antigens (HLA) holds the key to unlock target-specific cancer immunotherapies such as adoptive cell therapies or bispecific T cell engaging receptors. Therefore, comprehensive and accurate characterization of HLA peptidomes by mass spectrometry (immunopeptidomics) across tissues and disease states is essential. With growing numbers of immunopeptidomics datasets and the scope of peptide identification strategies reaching beyond the canonical proteome, the likelihood for erroneous peptide identification as well as false annotation of HLA-independent peptides as HLA ligands is increasing. Such "fake ligands" can lead to selection of nonexistent targets for immunotherapeutic development and need to be recognized as such as early as possible in the preclinical pipeline. Here we present computational and experimental methods that enable the identification of "fake ligands" that might be introduced at different steps of the immunopeptidomics workflow. The statistics presented herein allow discrimination of true HLA ligands from coisolated HLA-independent proteolytic fragments. In addition, we describe necessary steps to ensure system suitability of the chromatographic system. Furthermore, we illustrate an algorithm for detection of source fragmentation events that are introduced by electrospray ionization during mass spectrometry. For confirmation of peptide sequences, we present an experimental pipeline that enables high-throughput sequence verification through similarity of fragmentation pattern and coelution of synthetic isotope-labeled internal standards. Based on these methods, we show the overall high quality of existing datasets but point out limitations and pitfalls critical for individual peptides and how they can be uncovered in order to identify true ligands.

Translating Immunopeptidomics to Immunotherapy-Decision-Making for Patient and Personalized Target Selection.

Immunotherapy is revolutionizing cancer treatment and has shown success in particular for tumors with a high mutational load. These effects have been linked to neoantigens derived from patient-specific mutations. To expand efficacious immunotherapy approaches to the vast majority of tumor types and patient populations carrying only a few mutations and maybe not a single presented neoepitope, it is necessary to expand the target space to non-mutated cancer-associated antigens. Mass spectrometry enables the direct and unbiased discovery and selection of tumor-specific human leukocyte antigen (HLA) peptides that can be used to define targets for immunotherapy. Combining these targets into a warehouse allows for multi-target therapy and accelerated clinical application. For precise personalization aimed at optimally ensuring treatment efficacy and safety, it is necessary to assess the presence of the target on each individual patient's tumor. Here we show how LC-MS paired with gene expression data was used to define mRNA biomarkers currently being used as diagnostic test IMADETECT™ for patient inclusion and personalized target selection within two clinical trials (NCT02876510, NCT03247309). Thus, we present a way how to translate HLA peptide presentation into gene expression thresholds for companion diagnostics in immunotherapy considering the peptide-specific correlation to its encoding mRNA.

Mesenchyme-derived factors enhance preneoplastic growth by non-genotoxic carcinogens in rat liver.

Many frequently prescribed drugs are non-genotoxic carcinogens (NGC) in rodent liver. Their mode of action and health risks for humans remain to be elucidated. Here, we investigated the impact of two model NGC, the anti-epileptic drug phenobarbital (PB) and the contraceptive cyproterone acetate (CPA), on intrahepatic epithelial-mesenchymal crosstalk and on growth of first stages of hepatocarcinogenesis. Unaltered hepatocytes (HC) and preneoplastic HC (HCPREN) were isolated from rat liver for primary culture. DNA replication of HC and HCPREN was increased by in vitro treatment with 10 µM CPA, but not 1 mM PB. Next, mesenchymal cells (MC) obtained from liver of rats treated with either PB (50 mg/kg bw/day) or CPA (100 mg/kg bw/day), were cultured. Supernatants from both types of MC raised DNA synthesis of HC and HCPREN. This indicates that PB induces replication of HC and HCPREN only indirectly, via growth factors secreted by MC. CPA, however, acts on HC and HCPREN directly as well as indirectly via mesenchymal factors. Transcriptomics and bio-informatics revealed that PB and CPA induce extensive changes in the expression profile of MC affecting many growth factors and pathways. MC from PB-treated rats produced and secreted enhanced levels of HBEGF and GDF15, factors found to suppress apoptosis and/or induce DNA synthesis in cultured HC and HCPREN. MC from CPA-treated animals showed enhanced expression and secretion of HGF, which strongly raised DNA replication of HC and HCPREN. In conclusion, our findings reveal profound effects of two prototypical NGC on the hepatic mesenchyme. The resulting release of factors, which suppress apoptosis and/or enhance cell replication preferentially in cancer prestages, appears to be crucial for tumor promotion by NGC in the liver.

Epidermal Growth Factor Represses Constitutive Androstane Receptor Expression in Primary Human Hepatocytes and Favors Regulation by Pregnane X Receptor.

Growth factors have key roles in liver physiology and pathology, particularly by promoting cell proliferation and growth. Recently, it has been shown that in mouse hepatocytes, epidermal growth factor receptor (EGFR) plays a crucial role in the activation of the xenosensor constitutive androstane receptor (CAR) by the antiepileptic drug phenobarbital. Due to the species selectivity of CAR signaling, here we investigated epidermal growth factor (EGF) role in CAR signaling in primary human hepatocytes. Primary human hepatocytes were incubated with CITCO, a human CAR agonist, or with phenobarbital, an indirect CAR activator, in the presence or absence of EGF. CAR-dependent gene expression modulation and PXR involvement in these responses were assessed upon siRNA-based silencing of the genes that encode CAR and PXR. EGF significantly reduced CAR expression and prevented gene induction by CITCO and, to a lower extent, by phenobarbital. In the absence of EGF, phenobarbital and CITCO modulated the expression of 144 and 111 genes, respectively, in primary human hepatocytes. Among these genes, only 15 were regulated by CITCO and one by phenobarbital in a CAR-dependent manner. Conversely, in the presence of EGF, CITCO and phenobarbital modulated gene expression only in a CAR-independent and PXR-dependent manner. Overall, our findings suggest that in primary human hepatocytes, EGF suppresses specifically CAR signaling mainly through transcriptional regulation and drives the xenobiotic response toward a pregnane X receptor (PXR)-mediated mechanism.

Xenobiotic CAR Activators Induce Dlk1-Dio3 Locus Noncoding RNA Expression in Mouse Liver.

Derisking xenobiotic-induced nongenotoxic carcinogenesis (NGC) represents a significant challenge during the safety assessment of chemicals and therapeutic drugs. The identification of robust mechanism-based NGC biomarkers has the potential to enhance cancer hazard identification. We previously demonstrated Constitutive Androstane Receptor (CAR) and WNT signaling-dependent up-regulation of the pluripotency associated Dlk1-Dio3 imprinted gene cluster noncoding RNAs (ncRNAs) in the liver of mice treated with tumor-promoting doses of phenobarbital (PB). Here, we have compared phenotypic, transcriptional ,and proteomic data from wild-type, CAR/PXR double knock-out and CAR/PXR double humanized mice treated with either PB or chlordane, and show that hepatic Dlk1-Dio3 locus long ncRNAs are upregulated in a CAR/PXR-dependent manner by two structurally distinct CAR activators. We further explored the specificity of Dlk1-Dio3 locus ncRNAs as hepatic NGC biomarkers in mice treated with additional compounds working through distinct NGC modes of action. We propose that up-regulation of Dlk1-Dio3 cluster ncRNAs can serve as an early biomarker for CAR activator-induced nongenotoxic hepatocarcinogenesis and thus may contribute to mechanism-based assessments of carcinogenicity risk for chemicals and novel therapeutics.

Coma of unknown origin in the emergency department: implementation of an in-house management routine.

BACKGROUND: Coma of unknown origin is an emergency caused by a variety of possibly life-threatening pathologies. Although lethality is high, there are currently no generally accepted management guidelines. METHODS: We implemented a new interdisciplinary standard operating procedure (SOP) for patients presenting with non-traumatic coma of unknown origin. It includes a new in-house triage process, a new alert call, a new composition of the clinical response team and a new management algorithm (altogether termed "coma alarm"). It is triggered by two simple criteria to be checked with out-of-hospital emergency response teams before the patient arrives. A neurologist in collaboration with an internal specialist leads the in-hospital team. Collaboration with anaesthesiology, trauma surgery and neurosurgery is organised along structured pathways that include standardised laboratory tests and imaging. Patients were prospectively enrolled. We calculated response times as well as sensitivity and false positive rates, thus proportions of over- and undertriaged patients, as quality measures for the implementation in the SOP. RESULTS: During 24 months after implementation, we identified 325 eligible patients. Sensitivity was 60 % initially (months 1-4), then fluctuated between 84 and 94 % (months 5-24). Overtriage never exceeded 15 % and undertriage could be kept low at a maximum of 11 % after a learning period. We achieved a median door-to-CT time of 20 minutes. 85 % of patients needed subsequent ICU treatment, 40 % of which required specialised neuro-ICUs. DISCUSSION: Our results indicate that our new simple in-house triage criteria may be sufficient to identify eligible patients before arrival. We aimed at ensuring the fastest possible proceedings given high portions of underlying time-sensitive neurological and medical pathologies while using all available resources as purposefully as possible. CONCLUSIONS: Our SOP may provide an appropriate tool for efficient management of patients with non-traumatic coma. Our results justify the assignment of the initial diagnostic workup to neurologists and internal specialists in collaboration with anaesthesiologists.

ZBIT Bioinformatics Toolbox: A Web-Platform for Systems Biology and Expression Data Analysis.

Bioinformatics analysis has become an integral part of research in biology. However, installation and use of scientific software can be difficult and often requires technical expert knowledge. Reasons are dependencies on certain operating systems or required third-party libraries, missing graphical user interfaces and documentation, or nonstandard input and output formats. In order to make bioinformatics software easily accessible to researchers, we here present a web-based platform. The Center for Bioinformatics Tuebingen (ZBIT) Bioinformatics Toolbox provides web-based access to a collection of bioinformatics tools developed for systems biology, protein sequence annotation, and expression data analysis. Currently, the collection encompasses software for conversion and processing of community standards SBML and BioPAX, transcription factor analysis, and analysis of microarray data from transcriptomics and proteomics studies. All tools are hosted on a customized Galaxy instance and run on a dedicated computation cluster. Users only need a web browser and an active internet connection in order to benefit from this service. The web platform is designed to facilitate the usage of the bioinformatics tools for researchers without advanced technical background. Users can combine tools for complex analyses or use predefined, customizable workflows. All results are stored persistently and reproducible. For each tool, we provide documentation, tutorials, and example data to maximize usability. The ZBIT Bioinformatics Toolbox is freely available at https://webservices.cs.uni-tuebingen.de/.

Tumor promotion and inhibition by phenobarbital in livers of conditional Apc-deficient mice.

Activation of Wnt/ß-catenin signaling is important for human and rodent hepatocarcinogenesis. In mice, the tumor promoter phenobarbital (PB) selects for hepatocellular tumors with activating ß-catenin mutations via constitutive androstane receptor activation. PB-dependent tumor promotion was studied in mice with genetic inactivation of Apc, a negative regulator of ß-catenin, to circumvent the problem of randomly induced mutations by chemical initiators and to allow monitoring of PB- and Wnt/ß-catenin-dependent tumorigenesis in the absence of unknown genomic alterations. Moreover, the study was designed to investigate PB-induced proliferation of liver cells with activated ß-catenin. PB treatment provided Apc-deficient hepatocytes with only a minor proliferative advantage, and additional connexin 32 deficiency did not affect the proliferative response. PB significantly promoted the outgrowth of Apc-deficient hepatocellular adenoma (HCA), but simultaneously inhibited the formation of Apc-deficient hepatocellular carcinoma (HCC). The probability of tumor promotion by PB was calculated to be much lower for hepatocytes with loss of Apc, as compared to mutational ß-catenin activation. Comprehensive transcriptomic and phosphoproteomic characterization of HCA and HCC revealed molecular details of the two tumor types. HCC were characterized by a loss of differentiated hepatocellular gene expression, enhanced proliferative signaling, and massive over-activation of Wnt/ß-catenin signaling. In conclusion, PB exerts a dual role in liver tumor formation by promoting the growth of HCA but inhibiting the growth of HCC. Data demonstrate that one and the same compound can produce opposite effects on hepatocarcinogenesis, depending on context, highlighting the necessity to develop a more differentiated view on the tumorigenicity of this model compound.

Influence of Feature Encoding and Choice of Classifier on Disease Risk Prediction in Genome-Wide Association Studies.

Various attempts have been made to predict the individual disease risk based on genotype data from genome-wide association studies (GWAS). However, most studies only investigated one or two classification algorithms and feature encoding schemes. In this study, we applied seven different classification algorithms on GWAS case-control data sets for seven different diseases to create models for disease risk prediction. Further, we used three different encoding schemes for the genotypes of single nucleotide polymorphisms (SNPs) and investigated their influence on the predictive performance of these models. Our study suggests that an additive encoding of the SNP data should be the preferred encoding scheme, as it proved to yield the best predictive performances for all algorithms and data sets. Furthermore, our results showed that the differences between most state-of-the-art classification algorithms are not statistically significant. Consequently, we recommend to prefer algorithms with simple models like the linear support vector machine (SVM) as they allow for better subsequent interpretation without significant loss of accuracy.

Dysregulated serum response factor triggers formation of hepatocellular carcinoma.

UNLABELLED: The ubiquitously expressed transcriptional regulator serum response factor (SRF) is controlled by both Ras/MAPK (mitogen-activated protein kinase) and Rho/actin signaling pathways, which are frequently activated in hepatocellular carcinoma (HCC). We generated SRF-VP16iHep mice, which conditionally express constitutively active SRF-VP16 in hepatocytes, thereby controlling subsets of both Ras/MAPK- and Rho/actin-stimulated target genes. All SRF-VP16iHep mice develop hyperproliferative liver nodules that progresses to lethal HCC. Some murine (m)HCCs acquire Ctnnb1 mutations equivalent to those in human (h)HCC. The resulting transcript signatures mirror those of a distinct subgroup of hHCCs, with shared activation of oncofetal genes including Igf2, correlating with CpG hypomethylation at the imprinted Igf2/H19 locus. CONCLUSION: SRF-VP16iHep mHCC reveal convergent Ras/MAPK and Rho/actin signaling as a highly oncogenic driver mechanism for hepatocarcinogenesis. This suggests simultaneous inhibition of Ras/MAPK and Rho/actin signaling as a treatment strategy in hHCC therapy.

ToxDBScan: Large-scale similarity screening of toxicological databases for drug candidates.

We present a new tool for hepatocarcinogenicity evaluation of drug candidates in rodents. ToxDBScan is a web tool offering quick and easy similarity screening of new drug candidates against two large-scale public databases, which contain expression profiles for substances with known carcinogenic profiles: TG-GATEs and DrugMatrix. ToxDBScan uses a set similarity score that computes the putative similarity based on similar expression of genes to identify chemicals with similar genotoxic and hepatocarcinogenic potential. We propose using a discretized representation of expression profiles, which use only information on up- or down-regulation of genes as relevant features. Therefore, only the deregulated genes are required as input. ToxDBScan provides an extensive report on similar compounds, which includes additional information on compounds, differential genes and pathway enrichments. We evaluated ToxDBScan with expression data from 15 chemicals with known hepatocarcinogenic potential and observed a sensitivity of 88 Based on the identified chemicals, we achieved perfect classification of the independent test set. ToxDBScan is publicly available from the ZBIT Bioinformatics Toolbox.

Cross-platform toxicogenomics for the prediction of non-genotoxic hepatocarcinogenesis in rat.

In the area of omics profiling in toxicology, i.e. toxicogenomics, characteristic molecular profiles have previously been incorporated into prediction models for early assessment of a carcinogenic potential and mechanism-based classification of compounds. Traditionally, the biomarker signatures used for model construction were derived from individual high-throughput techniques, such as microarrays designed for monitoring global mRNA expression. In this study, we built predictive models by integrating omics data across complementary microarray platforms and introduced new concepts for modeling of pathway alterations and molecular interactions between multiple biological layers. We trained and evaluated diverse machine learning-based models, differing in the incorporated features and learning algorithms on a cross-omics dataset encompassing mRNA, miRNA, and protein expression profiles obtained from rat liver samples treated with a heterogeneous set of substances. Most of these compounds could be unambiguously classified as genotoxic carcinogens, non-genotoxic carcinogens, or non-hepatocarcinogens based on evidence from published studies. Since mixed characteristics were reported for the compounds Cyproterone acetate, Thioacetamide, and Wy-14643, we reclassified these compounds as either genotoxic or non-genotoxic carcinogens based on their molecular profiles. Evaluating our toxicogenomics models in a repeated external cross-validation procedure, we demonstrated that the prediction accuracy of our models could be increased by joining the biomarker signatures across multiple biological layers and by adding complex features derived from cross-platform integration of the omics data. Furthermore, we found that adding these features resulted in a better separation of the compound classes and a more confident reclassification of the three undefined compounds as non-genotoxic carcinogens.

Evaluation of toxicogenomics approaches for assessing the risk of nongenotoxic carcinogenicity in rat liver.

The current gold-standard method for cancer safety assessment of drugs is a rodent two-year bioassay, which is associated with significant costs and requires testing a high number of animals over lifetime. Due to the absence of a comprehensive set of short-term assays predicting carcinogenicity, new approaches are currently being evaluated. One promising approach is toxicogenomics, which by virtue of genome-wide molecular profiling after compound treatment can lead to an increased mechanistic understanding, and potentially allow for the prediction of a carcinogenic potential via mathematical modeling. The latter typically involves the extraction of informative genes from omics datasets, which can be used to construct generalizable models allowing for the early classification of compounds with unknown carcinogenic potential. Here we formally describe and compare two novel methodologies for the reproducible extraction of characteristic mRNA signatures, which were employed to capture specific gene expression changes observed for nongenotoxic carcinogens. While the first method integrates multiple gene rankings, generated by diverse algorithms applied to data from different subsamplings of the training compounds, the second approach employs a statistical ratio for the identification of informative genes. Both methods were evaluated on a dataset obtained from the toxicogenomics database TG-GATEs to predict the outcome of a two-year bioassay based on profiles from 14-day treatments. Additionally, we applied our methods to datasets from previous studies and showed that the derived prediction models are on average more accurate than those built from the original signatures. The selected genes were mostly related to p53 signaling and to specific changes in anabolic processes or energy metabolism, which are typically observed in tumor cells. Among the genes most frequently incorporated into prediction models were Phlda3, Cdkn1a, Akr7a3, Ccng1 and Abcb4.

Dynamic redistribution of calcium sensitive potassium channels (hK(Ca)3.1) in migrating cells.

Calcium-sensitive potassium channels (K(Ca)3.1) are expressed in virtually all migrating cells. Their activity is required for optimal cell migration so that their blockade leads to slowing down. K(Ca)3.1 channels must be inserted into the plasma membrane in order to elicit their physiological function. However, the plasma membrane of migrating cells is subject to rapid recycling by means of endo- and exocytosis. Here, we focussed on the endocytic internalization and the intracellular transport of the human isoform hK(Ca)3.1. A hK(Ca)3.1 channel construct with an HA-tag in the extracellularly located S3-S4 linker was transfected into migrating transformed renal epithelial MDCK-F cells. Channel internalization was visualized and quantified with immunofluorescence and a cell-based ELISA. Movement of hK(Ca)3.1 channel containing vesicles as well as migration of MDCK-F cells were monitored by means of time lapse video microscopy. hK(Ca)3.1 channels are endocytosed during migration. Most of the hK(Ca)3.1 channel containing vesicles are moving at a speed of up to 2 µm/sec in a microtubule-dependent manner towards the front of MDCK-F cells. Our experiments indicate that endocytosis of hK(Ca)3.1 channels is clathrin-dependent since they colocalize with clathrin adaptor proteins and since it is impaired when a C-terminal dileucine motif is mutated. The C-terminal dileucine motif is also important for the subcellular localization of hK(Ca)3.1 channels in migrating cells. Mutated channels are no longer concentrated at the leading edge. We therefore propose that recycling of hK(Ca)3.1 channels contributes to their characteristic subcellular distribution in migrating cells.

Substrate oxidation influences liking, wanting, macronutrient selection, and consumption of food in humans.

BACKGROUND: Several carbohydrate-based models of feeding have been described. The influence of the substrate oxidation rate on liking, wanting, and macronutrient selection, however, is not known in humans. OBJECTIVE: The aim of this study was to investigate the influence of the substrate oxidation rate on the above variables. DESIGN: A randomized 4-condition study was conducted in 16 normal-weight men (mean ± SD age: 23 ± 3 y). The sessions differed in the composition of breakfast, which was either high in carbohydrates (HC) or low in carbohydrates (LC) or high in fat (HF) or low in fat (LF). Two hours and 20 minutes after breakfast, energy expenditure (EE) and respiratory exchange ratios (RERs) were measured. Next, olfactory liking for 4 foods (sweet and fatty) and ad libitum energy intake (carbohydrate- and fat-rich bread) were evaluated. RESULTS: EE was higher (P < 0.001) and subsequent intake was lower (P < 0.01) after the HC and HF breakfasts than after the LC and LF breakfasts. The HC and LC breakfasts induced a higher RER (P < 0.001), lower olfactory liking for sweet foods (P < 0.05), and the consumption of a lower proportion of carbohydrate-rich bread (P< 0.05) than did the HF and LF breakfasts. The HF breakfast induced the lowest RER (P < 0.001), the lowest olfactory liking for fatty foods (P < 0.05), and the lowest proportion of fat-rich bread consumed (P < 0.01). Above all, a negative correlation was found between the RER and olfactory liking for sweet foods (P < 0.001). CONCLUSION: A high fat oxidation rate induces a strong liking for carbohydrates and a low liking for fats, which lends new support to the carbohydrate-based model of feeding. This trial is registered at clinicaltrials.gov as NCT01122082.

Acute partial sleep deprivation increases food intake in healthy men.

BACKGROUND: Acute partial sleep deprivation increases plasma concentrations of ghrelin and decreases those of leptin. OBJECTIVE: The objective was to observe modifications in energy intake and physical activity after acute partial sleep deprivation in healthy men. DESIGN: Twelve men [age: 22 +/- 3 y; body mass index (in kg/m(2)): 22.30 +/- 1.83] completed a randomized 2-condition crossover study. During the first night of each 48-h session, subjects had either approximately 8 h (from midnight to 0800) or approximately 4 h (from 0200 to 0600) of sleep. All foods consumed subsequently (jam on buttered toast for breakfast, buffet for lunch, and a free menu for dinner) were eaten ad libitum. Physical activity was recorded by an actimeter. Feelings of hunger, perceived pleasantness of the foods, desire to eat some foods, and sensation of sleepiness were also evaluated. RESULTS: In comparison with the 8-h sleep session, subjects consumed 559 +/- 617 kcal (ie, 22%) more energy on the day after sleep restriction (P < 0.01), and preprandial hunger was higher before breakfast (P < 0.001) and dinner (P < 0.05). No change in the perceived pleasantness of the foods or in the desire to eat the foods was observed. Physical activity from 1215 to 2015 was higher after sleep restriction than after 8 h of sleep (P < 0.01), even though the sensation of sleepiness was more marked (P < 0.01). CONCLUSIONS: One night of reduced sleep subsequently increased food intake and, to a lesser extent, estimated physical activity-related energy expenditure in healthy men. These experimental results, if confirmed by long-term energy balance measurements, suggest that sleep restriction could be a factor that promotes obesity. This trial was registered at clinicaltrials.gov as NCT00986492.

Alternation between foods within a meal. Influence on satiation and consumption in humans.

Food habituation/dishabituation has been observed in non-human primates in neurophysiological investigations of feeding, and in humans, through salivation or hedonic responses to food. The objective of the study was to evaluate in humans the effect of disruption of habituation by alternation between foods in a meal. Sixteen volunteers (8 males, 8 females; age: 21+/-1 yr; BMI: 21.5+/-0.5 kgm(-2)) ate a two-course meal [meatballs (M) and fries (F), then vanilla cream (C) and brownies (B)] during three randomized sessions. Sessions differed by the alternation of these foods: No-Repetition session with M-F-C-B; Single-Repetition session with F-M-F-B-C-B; Multiple-Repetition session with M-F-M-F-M-F-C-B-C-B-C-B. Final intakes of F and B were ad libitum. Quantities consumed (g, kJ) and ratings of hunger, pleasantness and desire to eat each food were evaluated. Compared to the No-Repetition session, subjects ate 18% more fries and 16% more brownies in the Single-Repetition, and 13% more fries but 20% less brownies in the Multiple-Repetition session. Pleasantness for the food decreased from before to after intake for both fries and brownies with no significant difference between the sessions. It therefore appears that moderate alternation between foods at lunch increases intake, but multiple alternations of foods at the end of the meal may decrease consumption. These differences in intakes could result from differences in sensory-specific satiety via disruption of habituation.