Microbial dysbiosis index for assessing colitis status in mouse models: A systematic review and meta-analysis.

Although countless gut microbiome studies on colitis using mouse models have been carried out, experiments with small sample sizes have encountered reproducibility limitations because of batch effects and statistical errors. In this study, dextran-sodium-sulfate-induced microbial dysbiosis index (DiMDI) was introduced as a reliable dysbiosis index that can be used to assess the state of microbial dysbiosis in DSS-induced mouse models. Meta-analysis of 189 datasets from 11 independent studies was performed to construct the DiMDI. Microbial dysbiosis biomarkers, Muribaculaceae, Alistipes, Turicibacter, and Bacteroides, were selected through four different feature selection methods and used to construct the DiMDI. This index demonstrated a high accuracy of 82.3% and showed strong robustness (88.9%) in the independent cohort. Therefore, DiMDI may be used as a standard for assessing microbial imbalance in DSS-induced mouse models and may contribute to the development of reliable colitis microbiome studies in mouse experiments.

The Alteration of the Gut Microbiome during Ramadan Offers a Novel Perspective on Ramadan Fasting: A Pilot Study.

An intermittent fasting regimen is widely perceived to lead to various beneficial health effects, including weight loss, the alleviation of insulin resistance, and the restructuring of a healthy gut microbiome. Because it shares certain commonalities with this dietary intervention, Ramadan fasting is sometimes misinterpreted as intermittent fasting, even though there are clear distinctions between these two regimens. The main purpose of this study is to verify whether Ramadan fasting drives the same beneficial effects as intermittent fasting by monitoring alterations in the gut microbiota. We conducted a study involving 20 Muslim individuals who were practicing Ramadan rituals and assessed the composition of their gut microbiomes during the 4-week period of Ramadan and the subsequent 8-week period post-Ramadan. Fecal microbiome analysis was conducted, and short-chain fatty acids (SCFAs) were assessed using liquid-chromatography-mass spectrometry. The observed decrease in the levels of SCFAs and beneficial bacteria during Ramadan, along with the increased microbial diversity post-Ramadan, suggests that the daily diet during Ramadan may not provide adequate nutrients to maintain robust gut microbiota. Additionally, the notable disparities in the functional genes detected through the metagenomic analysis and the strong correlation between Lactobacillus and SCFAs provide further support for our hypothesis.

Role, relevance, and possibilities of in vitro fermentation models in human dietary, and gut-microbial studies.

Dietary studies play a crucial role in determining the health-benefiting effects of most food substances, including prebiotics, probiotics, functional foods, and bioactive compounds. Such studies involve gastrointestinal digestion and colonic fermentation of dietary substances. In colonic fermentation, any digested food is further metabolized in the gut by the residing colonic microbiota, causing a shift in the gut microenvironment and production of various metabolites, such as short-chain fatty acids. These diet-induced shifts in the microbial community and metabolite production, which can be assessed through in vitro fermentation models using a donor's fecal microbiota, are well known to impact the health of the host. Although in vivo or animal experiments are the gold standard in dietary studies, recent advancements using different in vitro systems, like artificial colon (ARCOL), mini bioreactor array (MBRA), TNO in vitro model of the colon (TIM), Simulator of the Human Intestinal Microbial Ecosystem (SHIME), M-SHIME, Copenhagen MiniGut, and Dynamic Gastrointestinal Simulator, make it easy to study the dietary impact in terms of the gut microbiota and metabolites. Such a continuous in vitro system can have multiple compartments corresponding to different parts of the colon, that is, proximal, transverse, and distal colon, making the findings physiologically more significant. Furthermore, postfermentation samples can be analyzed using metagenomic, metabolomic, quantitative-polymerase chain reaction, and flow-cytometry approaches. Moreover, studies have shown that in vitro results are in accordance with the in vivo findings, supporting their relevance in dietary studies and giving confidence that shifts in metabolites are only due to microbes. This review meticulously describes the recent advancements in various fermentation models and their relevance in dietary studies.

Design of highly perceptible dual-resonance all-dielectric metasurface colorimetric sensor via deep neural networks.

Colorimetric sensing, which provides effective detection of bio-molecular signals with one's naked eye, is an exceptionally promising sensing technique in that it enables convenient detection and simplification of entire sensing system. Though colorimetric sensors based on all-dielectric nanostructures have potential to exhibit distinct color variations enabling manageable detection due to their trivial intrinsic loss, there is crucial limitation that the sensitivity to environmental changes lags behind their plasmonic counterparts because of relatively small region of near field-analyte interaction of the dielectric Mie-type resonator. To overcome this challenge, we proposed all-dielectric metasurface colorimetric sensor which exhibits dual-resonance in the visible region. Thereafter, we confirmed with simulation that, in the elaborately designed dual-Lorentzian-type spectra, highly perceptible variations of structural color were manifested even in minute change of peripheral refractive index. In addition to verifying physical effectiveness of the superior colorimetric sensing performance appearing in the dual-resonance type sensor, by combining advanced optimization technique utilizing deep neural networks, we attempted to maximize sensing performance while obtaining dramatic improvement of design efficiency. Through well-trained deep neural network that accurately simulates the input target spectrum, we numerically verified that designed colorimetric sensor shows a remarkable sensing resolution distinguishable up to change of refractive index of 0.0086.

Butyrate producers, "The Sentinel of Gut": Their intestinal significance with and beyond butyrate, and prospective use as microbial therapeutics.

Gut-microbial butyrate is a short-chain fatty acid (SCFA) of significant physiological importance than the other major SCFAs (acetate and propionate). Most butyrate producers belong to the Clostridium cluster of the phylum Firmicutes, such as Faecalibacterium, Roseburia, Eubacterium, Anaerostipes, Coprococcus, Subdoligranulum, and Anaerobutyricum. They metabolize carbohydrates via the butyryl-CoA: acetate CoA-transferase pathway and butyrate kinase terminal enzymes to produce most of butyrate. Although, in minor fractions, amino acids can also be utilized to generate butyrate via glutamate and lysine pathways. Butyrogenic microbes play a vital role in various gut-associated metabolisms. Butyrate is used by colonocytes to generate energy, stabilizes hypoxia-inducible factor to maintain the anaerobic environment in the gut, maintains gut barrier integrity by regulating Claudin-1 and synaptopodin expression, limits pro-inflammatory cytokines (IL-6, IL-12), and inhibits oncogenic pathways (Akt/ERK, Wnt, and TGF-ß signaling). Colonic butyrate producers shape the gut microbial community by secreting various anti-microbial substances, such as cathelicidins, reuterin, and ß-defensin-1, and maintain gut homeostasis by releasing anti-inflammatory molecules, such as IgA, vitamin B, and microbial anti-inflammatory molecules. Additionally, butyrate producers, such as Roseburia, produce anti-carcinogenic metabolites, such as shikimic acid and a precursor of conjugated linoleic acid. In this review, we summarized the significance of butyrate, critically examined the role and relevance of butyrate producers, and contextualized their importance as microbial therapeutics.

Anti-diabetic prospects of dietary bio-actives of millets and the significance of the gut microbiota: A case of finger millet.

Finger millet (Eleusine coracana) is a staple food in several parts of the world because of its high nutritional value. In addition to its high nutrient content, finger millet contains numerous bioactive compounds, including polyphenol (10.2 mg/g TAE), flavonoid (5.54 mg/g CE), phytic acid (0.48%), and dietary fiber (15-20%). Polyphenols are known for their anti-oxidant and anti-diabetic role. Phytic acid, previously considered an anti-nutritive substance, is now regarded as a nutraceutical as it reduces carbohydrate digestibility and thus controls post-prandial glucose levels and obesity. Thus, finger millet is an attractive diet for patients with diabetes. Recent findings have revealed that the anti-oxidant activity and bio-accessibility of finger millet polyphenols increased significantly (P < 0.05) in the colon, confirming the role of the gut microbiota. The prebiotic content of finger millet was also utilized by the gut microbiota, such as Faecalibacterium, Eubacterium, and Roseburia, to generate colonic short-chain fatty acids (SCFAs), and probiotic Bifidobacterium and Lactobacillus, which are known to be anti-diabetic in nature. Notably, finger millet-induced mucus-degrading Akkermansia muciniphila can also help in alleviate diabetes by releasing propionate and Amuc_1100 protein. Various millet bio-actives effectively controlled pathogenic gut microbiota, such as Shigella and Clostridium histolyticum, to lower gut inflammation and, thus, the risk of diabetes in the host. In the current review, we have meticulously examined the role of gut microbiota in the bio-accessibility of millet compounds and their impact on diabetes.

Absorptive metasurface color filters based on hyperbolic metamaterials for a CMOS image sensor.

Metasurface color filters (MCFs) have attracted considerable attention thanks to their compactness and functionality as a candidate of an optical element in a miniaturized image sensor. However, conventional dielectric and plasmonic MCFs that have focused on color purity and efficiency cannot avoid reflection in principle, which degrades image quality by optical flare. Here, we introduce absorptive-type MCFs through truncated-cone hyperbolic metamaterial absorbers. By applying a particle swarm optimization method to design multiple parameters simultaneously, the proposed MCF is theoretically and numerically demonstrated in perceptive color on CIELAB and CIEDE2000 with suppressed-reflection. Then, a color filter array is numerically proven in 255 nm of sub-pixel pitch.

Modulation of Gut Microbiota in Korean Navy Trainees following a Healthy Lifestyle Change.

Environmental factors can influence the composition of gut microbiota, but understanding the combined effect of lifestyle factors on adult gut microbiota is limited. Here, we investigated whether changes in the modifiable lifestyle factors, such as cigarette smoking, alcohol consumption, sleep duration, physical exercise, and body mass index affected the gut microbiota of Korean navy trainees. The navy trainees were instructed to stop smoking and alcohol consumption and follow a sleep schedule and physical exercise regime for eight weeks. For comparison, healthy Korean civilians, who had no significant change in lifestyles for eight weeks were included in this study. A total of 208 fecal samples were collected from navy trainees (n = 66) and civilians (n = 38) at baseline and week eight. Gut flora was assessed by sequencing the highly variable region of the 16S rRNA gene. The α-and ß -diversity of gut flora of both the test and control groups were not significantly changed after eight weeks. However, there was a significant difference among individuals. Smoking had a significant impact in altering α-diversity. Our study showed that a healthy lifestyle, particularly cessation of smoking, even in short periods, can affect the gut microbiome by enhancing the abundance of beneficial taxa and reducing that of harmful taxa.

A Multilayer-Learning Current-Mode Neuromorphic System With Analog-Error Compensation.

Internet-of-things applications that use machine-learning algorithms have increased the demand for application-specific energy-efficient hardware that can perform both learning and inference tasks to adapt to endpoint users or environmental changes. This paper presents a multilayer-learning neuromorphic system with analog-based multiplier-accumulator (MAC), which can learn training data by stochastic gradient descent algorithm. As a component of the proposed system, a current-mode MAC processor, fabricated in 28-nm CMOS technology, performs both forward and backward processing in a crossbar structure of 500 × 500 6-b transposable SRAM arrays. The proposed system is verified in a two-layer neural network by using two prototype chips and an FPGA. Without any calibration circuit for the analog-based MAC, the proposed system compensates for non-idealities from analog operations by learning training data with the analog-based MAC. With 1-b (+1, 0, -1) batch update of 6-b synaptic weights, the proposed system achieves a recognition rate of 96.6% with a peak energy efficiency of 2.99 TOPS/W (1 OP = one unsigned 8-b × signed 6-b MAC operation) in the classification of the MNIST dataset.

An On-Chip Learning Neuromorphic Autoencoder With Current-Mode Transposable Memory Read and Virtual Lookup Table.

This paper presents an IC implementation of on-chip learning neuromorphic autoencoder unit in a form of rate-based spiking neural network. With a current-mode signaling scheme embedded in a 500 × 500 6b SRAM-based memory, the proposed architecture achieves simultaneous processing of multiplications and accumulations. In addition, a transposable memory read for both forward and backward propagations and a virtual lookup table are also proposed to perform an unsupervised learning of restricted Boltzmann machine. The IC is fabricated using 28-nm CMOS process and is verified in a three-layer network of encoder-decoder pair for training and recovery of images with two-dimensional pixels. With a dataset of 50 digits, the IC shows a normalized root mean square error of 0.078. Measured energy efficiencies are 4.46 pJ per synaptic operation for inference and 19.26 pJ per synaptic weight update for learning, respectively. The learning performance is also estimated by simulations if the proposed hardware architecture is extended to apply to a batch training of 60 000 MNIST datasets.

Prevalence of Clostridium perfringens toxin in patients suspected of having antibiotic-associated diarrhea.

BACKGROUND: Although Clostridium perfringens has been reported as a cause of antibiotic-associated diarrhea (AAD), it is uncommon to detect this pathogen in clinical microbiology laboratories in Korea. The aim of this study was to investigate the prevalence of C. perfringens toxin in patients suspected of having AAD. METHODS: A total of 135 stool specimens submitted to a clinical microbiology laboratory for C. difficile toxin assay were tested. We tried to detect both C. difficile and C. perfringens toxins using the Seeplex Diarrhea ACE Detection kit (Seegene, Seoul, Korea). We evaluated the prevalence of 10 bacteria and 5 viruses. RESULTS: A total of 40 Clostridium spp. were detected in 34 specimens (29.6%). The C. perfringens toxin was detected in 14 of 135 specimens (10.4%), while C. difficile toxin was detected in 26 specimens (19.3%). Other bacteria and viruses, including 8 Aeromonas spp., were detected in 15 specimens. All tests were negative in 92 of the 135 specimens (68.1%). CONCLUSION: Clostridium perfringens toxin is relatively common, and we should consider the possibility of its presence in patients suspected of having AAD, especially if C. difficile tests are negative.

A Low-Power Wide Dynamic-Range Current Readout Circuit for Ion-Sensitive FET Sensors.

This paper presents an amplifier-less and digital-intensive current-to-digital converter for ion-sensitive FET sensors. Capacitance on the input node is utilized as a residue accumulator, and a clocked comparator is followed for quantization. Without any continuous-time feedback circuit, the converter performs a first-order noise shaping of the quantization error. In order to minimize static power consumption, the proposed circuit employs a single-ended current-steering digital-to-analog converter which flows only the same current as the input. By adopting a switching noise averaging algorithm, our dynamic element matching not only mitigates mismatch of current sources in the current-steering DAC, but also makes the effect of dynamic switching noise become an input-independent constant. The implemented circuit in 0.35 µm CMOS converts the current input with a range of 2.8 µ A to 15 b digital output in about 4 ms, showing a DNL of +0.24/-0.25 LSB and an INL of + 1.98/-1.98 LSB while consuming 16.8 µW.