Neutrophil degranulation, endothelial and metabolic dysfunction in unvaccinated long COVID patients.

BACKGROUND: Long COVID symptoms are widely diffused and have a poorly understood pathophysiology, with possible involvement of inflammatory cytokines. MATERIALS AND METHODS: A prospective follow-up study involved 385 unvaccinated patients, started 1 month after SARS-CoV-2 infection and continued for up to 12 months. We compared circulating biomarkers of neutrophil degranulation, endothelial and metabolic dysfunction in subjects with long COVID symptoms and in asymptomatic post-COVID controls. RESULTS: The highest occurrence of symptoms (71%) was after 3 months from the infection, decreasing to 62.3% and 29.4% at 6 and 12 months, respectively. Compared to controls, long COVID patients had increased levels of the neutrophilic degranulation indices MMP-8 and MPO, of endothelial dysfunction indices L-selectin and P-selectin. Among indices of metabolic dysfunction, leptin levels were higher in long COVID patients than in controls. CONCLUSION: In unvaccinated patients, symptoms may persist up to 1 year after acute COVID infection, with increased indices of neutrophil degranulation, endothelial and metabolic dysfunction. The clinical implications of specific inflammatory biomarkers require further attention, especially in individuals with fatigue and long COVID-linked cognitive dysfunctions.

Metabolic Dysfunction-Associated Steatotic Liver Disease: From Pathogenesis to Current Therapeutic Options.

The epidemiological burden of liver steatosis associated with metabolic diseases is continuously growing worldwide and in all age classes. This condition generates possible progression of liver damage (i.e., inflammation, fibrosis, cirrhosis, hepatocellular carcinoma) but also independently increases the risk of cardio-metabolic diseases and cancer. In recent years, the terminological evolution from "nonalcoholic fatty liver disease" (NAFLD) to "metabolic dysfunction-associated fatty liver disease" (MAFLD) and, finally, "metabolic dysfunction-associated steatotic liver disease" (MASLD) has been paralleled by increased knowledge of mechanisms linking local (i.e., hepatic) and systemic pathogenic pathways. As a consequence, the need for an appropriate classification of individual phenotypes has been oriented to the investigation of innovative therapeutic tools. Besides the well-known role for lifestyle change, a number of pharmacological approaches have been explored, ranging from antidiabetic drugs to agonists acting on the gut-liver axis and at a systemic level (mainly farnesoid X receptor (FXR) agonists, PPAR agonists, thyroid hormone receptor agonists), anti-fibrotic and anti-inflammatory agents. The intrinsically complex pathophysiological history of MASLD makes the selection of a single effective treatment a major challenge, so far. In this evolving scenario, the cooperation between different stakeholders (including subjects at risk, health professionals, and pharmaceutical industries) could significantly improve the management of disease and the implementation of primary and secondary prevention measures. The high healthcare burden associated with MASLD makes the search for new, effective, and safe drugs a major pressing need, together with an accurate characterization of individual phenotypes. Recent and promising advances indicate that we may soon enter the era of precise and personalized therapy for MASLD/MASH.

Effect of channel density, inverse solutions and connectivity measures on EEG resting-state networks reconstruction: A simulation study.

Along with the study of brain activity evoked by external stimuli, the past two decades witnessed an increased interest in characterizing the spontaneous brain activity occurring during resting conditions. The identification of connectivity patterns in this so-called "resting-state" has been the subject of a great number of electrophysiology-based studies, using the Electro/Magneto-Encephalography (EEG/MEG) source connectivity method. However, no consensus has been reached yet regarding a unified (if possible) analysis pipeline, and several involved parameters and methods require cautious tuning. This is particularly challenging when different analytical choices induce significant discrepancies in results and drawn conclusions, thereby hindering the reproducibility of neuroimaging research. Hence, our objective in this study was to shed light on the effect of analytical variability on outcome consistency by evaluating the implications of parameters involved in the EEG source connectivity analysis on the accuracy of resting-state networks (RSNs) reconstruction. We simulated, using neural mass models, EEG data corresponding to two RSNs, namely the default mode network (DMN) and dorsal attentional network (DAN). We investigated the impact of five channel densities (19, 32, 64, 128, 256), three inverse solutions (weighted minimum norm estimate (wMNE), exact low-resolution brain electromagnetic tomography (eLORETA), and linearly constrained minimum variance (LCMV) beamforming) and four functional connectivity measures (phase-locking value (PLV), phase-lag index (PLI), and amplitude envelope correlation (AEC) with and without source leakage correction), on the correspondence between reconstructed and reference networks. We showed that, with different analytical choices related to the number of electrodes, source reconstruction algorithm, and functional connectivity measure, high variability is present in the results. More specifically, our results show that a higher number of EEG channels significantly increased the accuracy of the reconstructed networks. Additionally, our results showed significant variability in the performance of the tested inverse solutions and connectivity measures. Such methodological variability and absence of analysis standardization represent a critical issue for neuroimaging studies that should be prioritized. We believe that this work could be useful for the field of electrophysiology connectomics, by increasing awareness regarding the challenge of variability in methodological approaches and its implications on reported results.

Ramadan intermittent fasting reduces visceral fat and improves gastrointestinal motility.

BACKGROUND: Ramadan is a model of intermittent fasting linked with possible beneficial effects. Scarce information, however, is available about the combined effects of Ramadan intermittent fasting (RIF) on anthropometric and metabolic indices, gastrointestinal symptoms, and motility. METHODS: In 21 healthy Muslims, we assessed the impact of RIF on caloric intake, physical activity, gastrointestinal symptoms and motility (gastric/gallbladder emptying by ultrasonography, orocaecal transit time by lactulose breath test), anthropometric indices, subcutaneous and visceral fat thickness (ultrasonography), glucose and lipid homeostasis. RESULTS: Mean caloric intake decreased from a median of 2069 kcal (range 1677-2641) before Ramadan to 1798 kcal (1289-3126) during Ramadan and increased again to 2000 kcal (1309-3485) after Ramadan. Although physical activity remained stable before, during, and after RIF, body weight, body mass index and waist circumference decreased in all subjects and in both genders, together with a significant decrease in subcutaneous and visceral fat thickness and insulin resistance. The postprandial gastric emptying speed was significantly faster after than before RIF. Fasting gallbladder volume was about 6% smaller after, than before Ramadan, with a stronger and faster postprandial gallbladder contraction. After RIF, lactulose breath test documented increased microbiota carbohydrate fermentation (postprandial H2 peak), and faster orocaecal transit time. RIF also significantly improved gastric fullness, epigastric pain and heartburn. CONCLUSIONS: RIF generates, in healthy subjects, multiple systemic beneficial effects in terms of fat burden, metabolic profile, gastrointestinal motility and symptoms. Further comprehensive studies should assess the potential beneficial effects of RIF in diseased people.

Contribution of the microbiome for better phenotyping of people living with obesity.

Obesity has reached epidemic proportion worldwide and in all ages. Available evidence points to a multifactorial pathogenesis involving gene predisposition and environmental factors. Gut microbiota plays a critical role as a major interface between external factors, i.e., diet, lifestyle, toxic chemicals, and internal mechanisms regulating energy and metabolic homeostasis, fat production and storage. A shift in microbiota composition is linked with overweight and obesity, with pathogenic mechanisms involving bacterial products and metabolites (mainly endocannabinoid-related mediators, short-chain fatty acids, bile acids, catabolites of tryptophan, lipopolysaccharides) and subsequent alterations in gut barrier, altered metabolic homeostasis, insulin resistance and chronic, low-grade inflammation. Although animal studies point to the links between an "obesogenic" microbiota and the development of different obesity phenotypes, the translational value of these results in humans is still limited by the heterogeneity among studies, the high variation of gut microbiota over time and the lack of robust longitudinal studies adequately considering inter-individual confounders. Nevertheless, available evidence underscores the existence of several genera predisposing to obesity or, conversely, to lean and metabolically health phenotype (e.g., Akkermansia muciniphila, species from genera Faecalibacterium, Alistipes, Roseburia). Further longitudinal studies using metagenomics, transcriptomics, proteomics, and metabolomics with exact characterization of confounders are needed in this field. Results must confirm that distinct genera and specific microbial-derived metabolites represent effective and precision interventions against overweight and obesity in the long-term.

Mean-Field Modeling of Brain-Scale Dynamics for the Evaluation of EEG Source-Space Networks.

Understanding the dynamics of brain-scale functional networks at rest and during cognitive tasks is the subject of intense research efforts to unveil fundamental principles of brain functions. To estimate these large-scale brain networks, the emergent method called "electroencephalography (EEG) source connectivity" has generated increasing interest in the network neuroscience community, due to its ability to identify cortical brain networks with satisfactory spatio-temporal resolution, while reducing mixing and volume conduction effects. However, no consensus has been reached yet regarding a unified EEG source connectivity pipeline, and several methodological issues have to be carefully accounted to avoid pitfalls. Thus, a validation toolbox that provides flexible "ground truth" models is needed for an objective methods/parameters evaluation and, thereby an optimization of the EEG source connectivity pipeline. In this paper, we show how a recently developed large-scale model of brain-scale activity, named COALIA, can provide to some extent such ground truth by providing realistic simulations of source-level and scalp-level activity. Using a bottom-up approach, the model bridges cortical micro-circuitry and large-scale network dynamics. Here, we provide an example of the potential use of COALIA to analyze, in the context of epileptiform activity, the effect of three key factors involved in the "EEG source connectivity" pipeline: (i) EEG sensors density, (ii) algorithm used to solve the inverse problem, and (iii) functional connectivity measure. Results showed that a high electrode density (at least 64 channels) is required to accurately estimate cortical networks. Regarding the inverse solution/connectivity measure combination, the best performance at high electrode density was obtained using the weighted minimum norm estimate (wMNE) combined with the weighted phase lag index (wPLI). Although those results are specific to the considered aforementioned context (epileptiform activity), we believe that this model-based approach can be successfully applied to other experimental questions/contexts. We aim at presenting a proof-of-concept of the interest of COALIA in the network neuroscience field, and its potential use in optimizing the EEG source-space network estimation pipeline.

Gut Microbiota and Short Chain Fatty Acids: Implications in Glucose Homeostasis.

Gut microbiota encompasses a wide variety of commensal microorganisms consisting of trillions of bacteria, fungi, and viruses. This microbial population coexists in symbiosis with the host, and related metabolites have profound effects on human health. In this respect, gut microbiota plays a pivotal role in the regulation of metabolic, endocrine, and immune functions. Bacterial metabolites include the short chain fatty acids (SCFAs) acetate (C2), propionate (C3), and butyrate (C4), which are the most abundant SCFAs in the human body and the most abundant anions in the colon. SCFAs are made from fermentation of dietary fiber and resistant starch in the gut. They modulate several metabolic pathways and are involved in obesity, insulin resistance, and type 2 diabetes. Thus, diet might influence gut microbiota composition and activity, SCFAs production, and metabolic effects. In this narrative review, we discuss the relevant research focusing on the relationship between gut microbiota, SCFAs, and glucose metabolism.

The Potential of Lamiaceae Herbs for Mitigation of Overweight, Obesity, and Fatty Liver: Studies and Perspectives.

Numerous plants, plant extracts, and plant-derived compounds are being explored for their beneficial effects against overweight and liver diseases. Obesity is associated with the increased prevalence of non-alcoholic fatty liver disease (NAFLD), becoming the most common liver disease in Western countries. Obesity and NAFLD are closely associated with many other metabolic alternations such as insulin resistance, diabetes mellitus, and cardiovascular diseases. Many herbs of the Lamiaceae family are widely employed as food and spices in the Mediterranean area, but also in folk medicine, and their use for the management of metabolic disorders is well documented. Hereby, we summarized the scientific results of the medicinal and nutraceutical potential of plants from the Lamiaceae family for prevention and mitigation of overweight and fatty liver. The evidence indicates that Lamiaceae plants may be a cost-effective source of nutraceuticals and/or phytochemicals to be used in the management of metabolic-related conditions such as obesity and NAFLD. PubMed, Google Scholar, Scopus, and SciFinder were accessed to collect data on traditional medicinal plants, compounds derived from plants, their reported anti-obesity mechanisms, and therapeutic targets.

Dynamics of task-related electrophysiological networks: a benchmarking study.

Motor, sensory and cognitive functions rely on dynamic reshaping of functional brain networks. Tracking these rapid changes is crucial to understand information processing in the brain, but challenging due to the great variety of dimensionality reduction methods used at the network-level and the limited evaluation studies. Using Magnetoencephalography (MEG) combined with Source Separation (SS) methods, we present an integrated framework to track fast dynamics of electrophysiological brain networks. We evaluate nine SS methods applied to three independent MEG databases (N=95) during motor and memory tasks. We report differences between these methods at the group and subject level. We seek to help researchers in choosing objectively the appropriate SS method when tracking fast reconfiguration of functional brain networks, due to its enormous benefits in cognitive and clinical neuroscience.

Antitumor Activity of Ethanolic Extract from Thymbra Spicata L. aerial Parts: Effects on Cell Viability and Proliferation, Apoptosis Induction, STAT3, and NF-kB Signaling.

Thyme-like plants including Thymbra spicata L. are widely used as food and folk medicinal remedies in the Mediterranean area. This study aimed to explore the in vitro antitumor potential of polyphenol-enriched extracts from aerial parts of T. spicata. The ethanolic extract significantly inhibited proliferation of different human tumor cell lines, without significant effects on non-neoplastic cells. A deeper investigation of the molecular mechanism sustaining the in vitro antitumor activity of the extract was carried on the human breast cancer cells MCF-7 in comparison with the normal breast cells MCF-10A. The effects on MCF-7 cells were associated with the following: (i) production of reactive oxygen species (ROS) and release of nitric oxide; (ii) apoptosis induction; and (iii) reduction in STAT3 and NF-kB phosphorylation. The ethanolic extract from T. spicata leaves might represent a novel therapeutic tool in combination with conventional chemotherapy to reduce the adverse side effects and drug resistance.

Extent and features of liver steatosis in vitro pave the way to endothelial dysfunction without physical cell-to-cell contact.

BACKGROUND AND AIMS: Several chronic multifactorial diseases originate from energy unbalance between food intake and body energy expenditure, including non-alcoholic fatty liver disease (NAFLD), diabetes, and cardiovascular disorders. Vascular endothelium plays a central role in body homeostasis, and NAFLD is often associated with endothelial dysfunction (ED), the first step in atherosclerosis. Both sugars and fatty acids (FAs) are fuel sources for energy production, but their excess leads to liver steatosis which may trigger ED through a network of mechanisms which need to be clarified. Here, we investigated the crosstalk pathways between in vitro cultured steatotic hepatocytes (FaO) and endothelial cells (HECV) being mediated by soluble factors. METHODS AND RESULTS: We employed the conditioned medium approach to test how different extent and features of hepatic steatosis distinctively affect endothelium leading to ED. The steatogenic media collected from steatotic hepatocytes were characterized by high triglyceride content and led to lipid accumulation and fat-dependent dysfunction in HECV cells. We found a parallelism between (i) extent of hepatocyte steatosis and level of lipid accumulation in HECV cells; (ii) type of hepatocyte steatosis (with macro- or microvesicular LDs) and extent of oxidative stress, lipid peroxidation, nitric oxide release and expression of ED markers in HECV cells. CONCLUSIONS: The present findings seem to suggest that, in addition to triglycerides, other soluble mediators should be released by steatotic hepatocytes and may influence lipid accumulation and function of HECV cells. Further studies need to depict the exact profile of soluble factors involved in steatotic hepatocyte-endothelium crosstalk.

Mitochondria Matter: Systemic Aspects of Nonalcoholic Fatty Liver Disease (NAFLD) and Diagnostic Assessment of Liver Function by Stable Isotope Dynamic Breath Tests.

The liver plays a key role in systemic metabolic processes, which include detoxification, synthesis, storage, and export of carbohydrates, lipids, and proteins. The raising trends of obesity and metabolic disorders worldwide is often associated with the nonalcoholic fatty liver disease (NAFLD), which has become the most frequent type of chronic liver disorder with risk of progression to cirrhosis and hepatocellular carcinoma. Liver mitochondria play a key role in degrading the pathways of carbohydrates, proteins, lipids, and xenobiotics, and to provide energy for the body cells. The morphological and functional integrity of mitochondria guarantee the proper functioning of ß-oxidation of free fatty acids and of the tricarboxylic acid cycle. Evaluation of the liver in clinical medicine needs to be accurate in NAFLD patients and includes history, physical exam, imaging, and laboratory assays. Evaluation of mitochondrial function in chronic liver disease and NAFLD is now possible by novel diagnostic tools. "Dynamic" liver function tests include the breath test (BT) based on the use of substrates marked with the non-radioactive, naturally occurring stable isotope 13C. Hepatocellular metabolization of the substrate will generate 13CO2, which is excreted in breath and measured by mass spectrometry or infrared spectroscopy. Breath levels of 13CO2 are biomarkers of specific metabolic processes occurring in the hepatocyte cytosol, microsomes, and mitochondria. 13C-BTs explore distinct chronic liver diseases including simple liver steatosis, non-alcoholic steatohepatitis, liver fibrosis, cirrhosis, hepatocellular carcinoma, drug, and alcohol effects. In NAFLD, 13C-BT use substrates such as α-ketoisocaproic acid, methionine, and octanoic acid to assess mitochondrial oxidation capacity which can be impaired at an early stage of disease. 13C-BTs represent an indirect, cost-effective, and easy method to evaluate dynamic liver function. Further applications are expected in clinical medicine. In this review, we discuss the involvement of liver mitochondria in the progression of NAFLD, together with the role of 13C-BT in assessing mitochondrial function and its potential use in the prevention and management of NAFLD.

Single-shot, omni-directional x-ray scattering imaging with a laboratory source and single-photon localization.

Omni-directional, ultra-small-angle x-ray scattering imaging provides a method to measure the orientation of micro-structures without having to resolve them. In this letter, we use single-photon localization with the Timepix3 chip to demonstrate, to the best of our knowledge, the first laboratory-based implementation of single-shot, omni-directional x-ray scattering imaging using the beam-tracking technique. The setup allows a fast and accurate retrieval of the scattering signal using a simple absorption mask. We suggest that our new approach may enable faster laboratory-based tensor tomography and could be used for energy-resolved x-ray scattering imaging.

New Perspectives of S-Adenosylmethionine (SAMe) Applications to Attenuate Fatty Acid-Induced Steatosis and Oxidative Stress in Hepatic and Endothelial Cells.

S-adenosylmethionine (SAMe) is an endogenous methyl donor derived from ATP and methionine that has pleiotropic functions. Most SAMe is synthetized and consumed in the liver, where it acts as the main methylating agent and in protection against the free radical toxicity. Previous studies have shown that the administration of SAMe as a supernutrient exerted many beneficial effects in various tissues, mainly in the liver. In the present study, we aimed to clarify the direct effects of SAMe on fatty acid-induced steatosis and oxidative stress in hepatic and endothelial cells. Hepatoma FaO cells and endothelial HECV cells exposed to a mixture of oleate/palmitate are reliable models for hepatic steatosis and endothelium dysfunction, respectively. Our findings indicate that SAMe was able to significantly ameliorate lipid accumulation and oxidative stress in hepatic cells, mainly through promoting mitochondrial fatty acid entry for ß-oxidation and external triglyceride release. SAMe also reverted both lipid accumulation and oxidant production (i.e., ROS and NO) in endothelial cells. In conclusion, these outcomes suggest promising beneficial applications of SAMe as a nutraceutical for metabolic disorders occurring in fatty liver and endothelium dysfunction.

Designing Two Secure Keyed Hash Functions Based on Sponge Construction and the Chaotic Neural Network.

In this paper, we propose, implement, and analyze the structures of two keyed hash functions using the Chaotic Neural Network (CNN). These structures are based on Sponge construction, and they produce two variants of hash value lengths, i.e., 256 and 512 bits. The first structure is composed of two-layered CNN, while the second one is formed by one-layered CNN and a combination of nonlinear functions. Indeed, the proposed structures employ two strong nonlinear systems, precisely a chaotic system and a neural network system. In addition, the proposed study is a new methodology of combining chaotic neural networks and Sponge construction that is proved secure against known attacks. The performance of the two proposed structures is analyzed in terms of security and speed. For the security measures, the number of hits of the two proposed structures doesn't exceed 2 for 256-bit hash values and does not exceed 3 for 512-bit hash values. In terms of speed, the average number of cycles to hash one data byte (NCpB) is equal to 50.30 for Structure 1, and 21.21 and 24.56 for Structure 2 with 8 and 24 rounds, respectively. In addition, the performance of the two proposed structures is compared with that of the standard hash functions SHA-3, SHA-2, and with other classical chaos-based hash functions in the literature. The results of cryptanalytic analysis and the statistical tests highlight the robustness of the proposed keyed hash functions. It also shows the suitability of the proposed hash functions for the application such as Message Authentication, Data Integrity, Digital Signature, and Authenticated Encryption with Associated Data.

Spatio-temporal analysis of urban growth and its impact on floods in Ajman City, UAE.

The negative consequences of urbanisation have been recently recognised despite the social and economic benefits it provides to the community. Effects of urbanisation include increases in surface runoff, frequency and magnitude of floods and urban water harvesting capacity. Accordingly, this study utilised multi-spectral and multi-resolution satellite images combined with field data to conduct a quantitative assessment of the impact of urbanisation on urban flooding for the period of 1975-2015 in Ajman City, United Arab Emirates (UAE). Results showed that urban areas in the city have increased by approximately 12-fold over the period 1975-2015, whilst the population increased by approximately 16-fold. Owing to a substantial increase in urbanisation (as impervious areas expanded), minimum precipitation to generate runoff in built areas dropped from approximately 16.37 mm in 1975 to approximately 13.3 mm in 2015, which caused a substantial increase in the surface runoff. To visualise the flooding potential, urban flooding maps were generated using a well-established decision analysis technique called Analytical Hierarchy Process. The latter adopted three thematic factors, namely excess rain, elevation and slope. Flooding potential was then found to have increased substantially, specifically in the downtown area. Finally, this study is expected to contribute highly to flood protection and sustainable urban storm water management in Ajman City.

Comparative Study of Three Steganographic Methods Using a Chaotic System and Their Universal Steganalysis Based on Three Feature Vectors.

In this paper, we firstly study the security enhancement of three steganographic methods by using a proposed chaotic system. The first method, namely the Enhanced Edge Adaptive Image Steganography Based on LSB Matching Revisited (EEALSBMR), is present in the spatial domain. The two other methods, the Enhanced Discrete Cosine Transform (EDCT) and Enhanced Discrete Wavelet transform (EDWT), are present in the frequency domain. The chaotic system is extremely robust and consists of a strong chaotic generator and a 2-D Cat map. Its main role is to secure the content of a message in case a message is detected. Secondly, three blind steganalysis methods, based on multi-resolution wavelet decomposition, are used to detect whether an embedded message is hidden in the tested image (stego image) or not (cover image). The steganalysis approach is based on the hypothesis that message-embedding schemes leave statistical evidence or structure in images that can be exploited for detection. The simulation results show that the Support Vector Machine (SVM) classifier and the Fisher Linear Discriminant (FLD) cannot distinguish between cover and stego images if the message size is smaller than 20% in the EEALSBMR steganographic method and if the message size is smaller than 15% in the EDCT steganographic method. However, SVM and FLD can distinguish between cover and stego images with reasonable accuracy in the EDWT steganographic method, irrespective of the message size.

Subpixel resolution in CdTe Timepix3 pixel detectors.

Timepix3 (256 × 256 pixels with a pitch of 55 µm) is a hybrid-pixel-detector readout chip that implements a data-driven architecture and is capable of simultaneous time-of-arrival (ToA) and energy (ToT: time-over-threshold) measurements. The ToA information allows the unambiguous identification of pixel clusters belonging to the same X-ray interaction, which allows for full one-by-one detection of photons. The weighted mean of the pixel clusters can be used to measure the subpixel position of an X-ray interaction. An experiment was performed at the European Synchrotron Radiation Facility in Grenoble, France, using a 5 µm × 5 µm pencil beam to scan a CdTe-ADVAPIX-Timepix3 pixel (55 µm × 55 µm) at 8 × 8 matrix positions with a step size of 5 µm. The head-on scan was carried out at four monochromatic energies: 24, 35, 70 and 120 keV. The subpixel position of every single photon in the beam was constructed using the weighted average of the charge spread of single interactions. Then the subpixel position of the total beam was found by calculating the mean position of all photons. This was carried out for all points in the 8 × 8 matrix of beam positions within a single pixel. The optimum conditions for the subpixel measurements are presented with regards to the cluster sizes and beam subpixel position, and the improvement of this technique is evaluated (using the charge sharing of each individual photon to achieve subpixel resolution) versus alternative techniques which compare the intensity ratio between pixels. The best result is achieved at 120 keV, where a beam step of 4.4 µm ± 0.86 µm was measured.

Fast multidimensional NMR spectroscopy for sparse spectra.

Multidimensional NMR spectroscopy is widely used for studies of molecular and biomolecular structure. A major disadvantage of multidimensional NMR is the long acquisition time which, regardless of sensitivity considerations, may be needed to obtain the final multidimensional frequency domain coefficients. In this article, a method for under-sampling multidimensional NMR acquisition of sparse spectra is presented. The approach is presented in the case of two-dimensional NMR acquisitions. It relies on prior knowledge about the support in the two-dimensional frequency domain to recover an over-determined system from the under-determined system induced in the linear acquisition model when under-sampled acquisitions are performed. This over-determined system can then be solved with linear least squares. The prior knowledge is obtained efficiently at a low cost from the one-dimensional NMR acquisition, which is generally acquired as a first step in multidimensional NMR. If this one-dimensional acquisition is intrinsically sparse, it is possible to reconstruct the corresponding two-dimensional acquisition from far fewer observations than those imposed by the Nyquist criterion, and subsequently to reduce the acquisition time. Further improvements are obtained by optimizing the sampling procedure for the least-squares reconstruction using the sequential backward selection algorithm. Theoretical and experimental results are given in the case of a traditional acquisition scheme, which demonstrate reliable and fast reconstructions with acceleration factors in the range 3-6. The proposed method outperforms the CS methods (OMP, L1) in terms of the reconstruction performance, implementation and computation time. The approach can be easily extended to higher dimensions and spectroscopic imaging.

Gut microbes in metabolic disturbances. Promising role for therapeutic manipulations?

The prevalence of overweight, obesity, type 2 diabetes, metabolic syndrome and steatotic liver disease is rapidly increasing worldwide with a huge economic burden in terms of morbidity and mortality. Several genetic and environmental factors are involved in the onset and development of metabolic disorders and related complications. A critical role also exists for the gut microbiota, a complex polymicrobial ecology at the interface of the internal and external environment. The gut microbiota contributes to food digestion and transformation, caloric intake, and immune response of the host, keeping the homeostatic control in health. Mechanisms of disease include enhanced energy extraction from the non-digestible dietary carbohydrates, increased gut permeability and translocation of bacterial metabolites which activate a chronic low-grade systemic inflammation and insulin resistance, as precursors of tangible metabolic disorders involving glucose and lipid homeostasis. The ultimate causative role of gut microbiota in this respect remains to be elucidated, as well as the therapeutic value of manipulating the gut microbiota by diet, pre- and pro- synbiotics, or fecal microbial transplantation.