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Abstract The probiotic gut microbiome and its metabolites are pivotal in regulating host metabolism, inflammation, and immunity. Host genetics, colonization at birth, the host lifestyle, and exposure to diseases and drugs determine microbial composition. Dysbiosis and disruption of homeostasis in the beneficial microbiome have been reported to be involved in the tumorigenesis and progression of colorectal cancer (CRC). However, the influence of bacteria-secreted metabolites on CRC growth is yet to be fully elucidated. In this study, we compared the microbial composition of CRC patients to healthy controls to identify distinct patterns of microbiota-derived metabolites in CRC patients. Metagenomic analysis demonstrated that beneficial bacteria strains; Blautia producta, Bifidobacterium adolescentis, and Bifidobacterium longum decreased, while Parabacteroides distasonis and Bacteroides ovatus were more prevalent in the CRC patient group. Treatment of cancer organoid lines with microbial culture supernatants from Blautia producta, Bifidobacterium adolescentis, and Bifidobacterium longum showed remarkable inhibition of cancer growth. This study demonstrates that the bacterial metabolites depleted in CRC patients may inhibit cancer growth and highlights the effects of microbiome-derived metabolites on CRC growth.

The small, simple, and effective characteristics of a Supercritical CO2 (S–CO2) power system make it an ideal candidate for distributed power generation. However, the distributed power generation will be required with constant changes in the operating conditions to meet the varying demand. Thus, it is necessary for the S–CO2 power system to demonstrate that it can operate safely and efficiently under all anticipated operating conditions. One of the key issues in the S–CO2 power system is controlling the compressor inlet conditions. This paper focused on the control of the compressor inlet temperature. In this paper, a system dynamic simulation code is used to model the precooler system for the compressor inlet control first. Based on the analysis results to obtain the transfer function and the state space, a linear quadratic controller was designed using optimal control theory. Since the controller was estimated to have steady-state error, a disturbance observer was also used. To validate the designed controller, experiments were conducted. The experimental results show that the proposed controller can be used to maintain the S–CO2 compressor inlet conditions under various transient scenarios without additional tuning.

Abstract There is an unmet need for biomarkers for the diagnosis of lung cancer and decision criteria for lung biopsy. We comparatively investigated the lung microbiomes of patients with lung cancer and benign lung diseases. Patients who underwent bronchoscopy at Chungnam National University Hospital between June 2021 and June 2022 were enrolled. Bronchoalveolar lavage fluid (BALF) was collected from 24 patients each with lung cancer and benign lung diseases. The samples were analyzed using 16S rRNA-based metagenomic sequencing. We found that alpha diversity and the beta diversity distribution (P = 0.001) differed significantly between patients with benign lung diseases and those with lung cancer. Firmicutes was the most abundant phylum in patients with lung cancer (33.39% ± 17.439), whereas Bacteroidota was the most abundant phylum in patients with benign lung disease (31.132% ± 22.505), respectively. In differential abundance analysis, the most differentially abundant microbiota taxon was unclassified_SAR202_clade, belonging to the phylum Chloroflexi. The established prediction model distinguished patients with benign lung disease from those with lung cancer with a high accuracy (micro area under the curve [AUC] = 0.98 and macro AUC = 0.99). The BALF microbiome may be a novel biomarker for the detection of lung cancer.

Abstract Although diabetes mellitus is a complex and pervasive disease, most studies to date have focused on individual features, rather than considering the complexities of multivariate, multi-instance, and time-series data. In this study, we developed a novel diabetes prediction model that incorporates these complex data types. We applied advanced techniques of data imputation (bidirectional recurrent imputation for time series; BRITS) and feature selection (the least absolute shrinkage and selection operator; LASSO). Additionally, we utilized self-supervised algorithms and transfer learning to address the common issues with medical datasets, such as irregular data collection and sparsity. We also proposed a novel approach for discrete time-series data preprocessing, utilizing both shifting and rolling time windows and modifying time resolution. Our study evaluated the performance of a progressive self-transfer network for predicting diabetes, which demonstrated a significant improvement in metrics compared to non-progressive and single self-transfer prediction tasks, particularly in AUC, recall, and F1 score. These findings suggest that the proposed approach can mitigate accumulated errors and reflect temporal information, making it an effective tool for accurate diagnosis and disease management. In summary, our study highlights the importance of considering the complexities of multivariate, multi-instance, and time-series data in diabetes prediction.

Flextensional transducers have been widely used as low-frequency projectors, and these characteristics can be used to develop hydrophones with wider receiver bandwidth and higher sensitivity than conventional products in low-frequency ranges. In this work, we designed flextensional hydrophones of all classes, and compared their acoustic receiver performance to select the most suitable class for a low-frequency broadband hydrophone. For this purpose, basic models of the hydrophones were constructed for all classes and the effects of various structural parameters on the acoustic receiver characteristics of the hydrophones were analyzed. Based on the results, the structure of the flextensional hydrophone of each class was designed to have the maximum receiver bandwidth by an optimization technique while maintaining the receiver voltage sensitivity over a certain level. A comparison of the designed performance led to the selection of the class IV flextensional hydrophone as the most promising one with the widest receiver fractional bandwidth and highest sensitivity.

Abstract In the medical field, various clinical information has been accumulated to help clinicians provide personalized medicine and make better diagnoses. As chronic diseases share similar characteristics, it is possible to predict multiple chronic diseases using the accumulated data of each patient. Thus, we propose an intra-person multi-task learning framework that jointly predicts the status of correlated chronic diseases and improves the model performance. Because chronic diseases occur over a long period and are affected by various factors, we considered features related to each chronic disease and the temporal relationship of the time-series data for accurate prediction. The study was carried out in three stages: (1) data preprocessing and feature selection using bidirectional recurrent imputation for time series (BRITS) and the least absolute shrinkage and selection operator (LASSO); (2) a convolutional neural network and long short-term memory (CNN-LSTM) for single-task models; and (3) a novel intra-person multi-task learning CNN-LSTM framework developed to predict multiple chronic diseases simultaneously. Our multi-task learning method between correlated chronic diseases produced a more stable and accurate system than single-task models and other baseline recurrent networks. Furthermore, the proposed model was tested using different time steps to illustrate its flexibility and generalization across multiple time steps.

Abstract Background Comparisons of the gut microbiome of lean and obese humans have revealed that obesity is associated with the gut microbiome plus changes in numerous environmental factors, including high-fat diet (HFD). Here, we report that two species of Bifidobacterium are crucial to controlling metabolic parameters in the Korean population. Results Based on gut microbial analysis from 99 Korean individuals, we observed the abundance of Bifidobacterium longum and Bifidobacterium bifidum was markedly reduced in individuals with increased visceral adipose tissue (VAT), body mass index (BMI), blood triglyceride (TG), and fatty liver. Bacterial transcriptomic analysis revealed that carbohydrate/nucleoside metabolic processes of Bifidobacterium longum and Bifidobacterium bifidum were associated with protecting against diet-induced obesity. Oral treatment of specific commercial Bifidobacterium longum and Bifidobacterium bifidum enhanced bile acid signaling contributing to potentiate oxidative phosphorylation (OXPHOS) in adipose tissues, leading to reduction of body weight gain and improvement in hepatic steatosis and glucose homeostasis. Bifidobacterium longum or Bifidobacterium bifidum manipulated intestinal sterol biosynthetic processes to protect against diet-induced obesity in germ-free mice. Conclusions Our findings support the notion that treatment of carbohydrate/nucleoside metabolic processes-enriched Bifidobacterium longum and Bifidobacterium bifidum would be a novel therapeutic strategy for reprograming the host metabolic homeostasis to protect against metabolic syndromes, including diet-induced obesity. Video Abstract

Instabilities such as stall and surge have been widely studied in open loop gas turbine system for several decades. However, researches on instabilities in a closed loop are limited. Especially, no experimental investigation in a supercritical CO2 compressor has been performed thoroughly before. An integral test loop equipped with a turbo-alternator-compressor using active magnetic bearing was utilized to study the instabilities in a supercritical CO2 compressor. The compressor performance testing was conducted including near stall and surge operating conditions. The mass flow rate of compressor was greatly reduced even beyond surge limit and pressure was measured with high frequency transducer to observe the characteristics. It was observed that as the inertia of the system was changed by altering the compressor inlet conditions, the characteristics of the supercritical CO2 compressor in unstable operating conditions also vary. This can be qualitatively explained by using the theory Greitzer proposed previously.

Abstract Immune checkpoint inhibitor (ICI) clinically benefits cancer treatment. However, the ICI responses are only achieved in a subset of patients, and the underlying mechanisms of the limited response remain unclear. 160 patients with non‐small cell lung cancer treated with anti‐programmed cell death protein‐1 (anti‐PD‐1) or anti‐programmed death ligand‐1 (anti‐PD‐L1) are analyzed to understand the early determinants of response to ICI. It is observed that high levels of intracellular adhesion molecule‐1 (ICAM‐1) in tumors and plasma of patients are associated with prolonged survival. Further reverse translational studies using murine syngeneic tumor models reveal that soluble ICAM‐1 (sICAM‐1) is a key molecule that increases the efficacy of anti‐PD‐1 via activation of cytotoxic T cells. Moreover, chemokine (CXC motif) ligand 13 (CXCL13) in tumors and plasma is correlated with the level of ICAM‐1 and ICI efficacy, suggesting that CXCL13 might be involved in the ICAM‐1‐mediated anti‐tumor pathway. Using sICAM‐1 alone and in combination with anti‐PD‐1 enhances anti‐tumor efficacy in anti‐PD‐1‐responsive tumors in murine models. Notably, combinatorial therapy with sICAM‐1 and anti‐PD‐1 converts anti‐PD‐1‐resistant tumors to responsive ones in a preclinical study. These findings provide a new immunotherapeutic strategy for treating cancers using ICAM‐1.

Predicting recurrence in patients with non-small cell lung cancer (NSCLC) before treatment is vital for guiding personalized medicine. Deep learning techniques have revolutionized the application of cancer informatics, including lung cancer time-to-event prediction. Most existing convolutional neural network (CNN) models are based on a single two-dimensional (2D) computational tomography (CT) image or three-dimensional (3D) CT volume. However, studies have shown that using multi-scale input and fusing multiple networks provide promising performance. This study proposes a deep learning-based ensemble network for recurrence prediction using a dataset of 530 patients with NSCLC. This network assembles 2D CNN models of various input slices, scales, and convolutional kernels, using a deep learning-based feature fusion model as an ensemble strategy. The proposed framework is uniquely designed to benefit from (i) multiple 2D in-plane slices to provide more information than a single central slice, (ii) multi-scale networks and multi-kernel networks to capture the local and peritumoral features, (iii) ensemble design to integrate features from various inputs and model architectures for final prediction. The ensemble of five 2D-CNN models, three slices, and two multi-kernel networks, using 5 × 5 and 6 × 6 convolutional kernels, achieved the best performance with an accuracy of 69.62%, area under the curve (AUC) of 72.5%, F1 score of 70.12%, and recall of 70.81%. Furthermore, the proposed method achieved competitive results compared with the 2D and 3D-CNN models for cancer outcome prediction in the benchmark studies. Our model is also a potential adjuvant treatment tool for identifying NSCLC patients with a high risk of recurrence.

Kim et al. show that several Streptococcus species improve the structure and barrier function of human skin. They find that Streptococcus-secreted spermidine accelerates the recovery of skin structure and barrier function by increasing collagen and lipid synthesis in aged cells. This study suggests the role of skin microbiome for anti-aging.

Obesity: Microbial defenders of metabolic health The presence of particular bacterial species in the healthy gut microbiome can reduce the risk of obesity by modulating metabolic activity in host tissues. Several studies have suggested that intestinal microbes from the genus Lactobacillus can counteract weight gain and fat accumulation in humans and mice. Researchers in South Korea led by Sungsoon Fang at Yonsei University College of Medicine, Seoul, and Hansoo Park at the Gwangju Institute of Science and Technology have now examined the physiological effects of one such species, Lactobacillus fermentum LM1016, in mice. The researchers showed that this bacterium could reduce the risk of diet-induced obesity and fat accumulation in the liver, and identified relevant shifts in the activity of key metabolic and inflammatory pathways. If these findings are confirmed in humans, this bacterial strain could serve as an effective probiotic.

Background Although some human studies have reported gut microbiome changes in individuals with Alzheimer’s disease (AD) dementia or mild cognitive impairment (MCI), gut microbiome alterations in preclinical AD, i.e., cerebral amyloidosis without cognitive impairment, is largely unknown. Objective We aimed to identify gut microbial alterations associated with preclinical AD by comparing cognitively normal (CN) older adults with cerebral Aβ deposition (Aβ+ CN) and those without cerebral Aβ deposition (Aβ− CN). Methods Seventy-eight CN older participants (18 Aβ+ CN and 60 Aβ− CN) were included, and all participants underwent clinical assessment and Pittsburg compound B–positron emission tomography. The V3–V4 region of the 16S rRNA gene of genomic DNA extracted from feces was amplified and sequenced to establish the microbial community. Results Generalized linear model analysis revealed that the genera Megamonas (B = 3.399, qConclusion Our findings suggest that specific alterations of gut bacterial taxa are related to preclinical AD, which means these changes may precede cognitive decline. Therefore, examining changes in the microbiome may be helpful in preclinical AD screening.

Remarkable progress has occurred over the past two decades in identifying microbiomes affecting the human body in numerous ways. The microbiome is linked to gastrointestinal (GI) tract cancer. The purpose of this study was to determine if there is a common microbiome among GI tract cancers and how the microbiome affects the disease. To ensure ethnic consistency, Korean patients with GI tract cancer were selected. Fusobacterium nucleatum is an enriched bacteria in all cancer tissues. F. nucleatum is a Gram-negative obligate anaerobe that promotes colorectal cancer. Through Gene Set Enrichment Analysis (GSEA) and Differentially Expressed Genes (DEG) analyses, the upregulation of the G2M checkpoint pathway was identified in the F. nucleatum-high group. Cell viability and G2M checkpoint pathway genes were examined in MC 38 cells treated with F. nucleatum. F. nucleatum upregulated the expression of G2M checkpoint pathway genes and the cell proliferation of MC 38 cells. F. nucleatum facilitated cancer’s use of G2M checkpoint pathways and F. nucleatum could be a therapeutic target in Korean GI tract cancer.

Due to high recurrence rates in patients with non-small cell lung cancer (NSCLC), medical professionals need extremely accurate diagnostic methods to prevent bleak prognoses. However, even the most commonly used diagnostic method, the TNM staging system, which describes the tumor-size, nodal-involvement, and presence of metastasis, is often inaccurate in predicting NSCLC recurrence. These limitations make it difficult for clinicians to tailor treatments to individual patients. Here, we propose a novel approach, which applies deep learning to an ensemble-based method that exploits patient-derived, multi-modal data. This will aid clinicians in successfully identifying patients at high risk of recurrence and improve treatment planning.

Exsolution of transition metals from perovskites provides an effective route to useful catalysts, but the underlying factors governing exsolution remain poorly understood. Here, the authors find trends in exsolution in layered perovskites, showing that co-segregation of B-site dopant and oxygen vacancies plays a central role.

The Cordyceps species has been a good source of compounds with anticancer and anti-inflammatory activities. Recently, we reported a novel compound (4-isopropyl-2,6-bis(1-phenylethyl)phenol, KTH-13) with anticancer activity isolated from Cordyceps bassiana and created several derivatives to increase its pharmacological activity. In this study, we tested one of the KTH-013 derivatives, 4-isopropyl-2,6-bis(1-phenylethyl)aniline 1 (KTH-13-AD1), with regard to anti-inflammatory activity under macrophage-mediated inflammatory conditions. KTH-13-AD1 clearly suppressed the production of nitric oxide (NO) and reactive oxygen species (ROS) in lipopolysaccharide (LPS) and sodium nitroprusside- (SNP-) treated macrophage-like cells (RAW264.7 cells). Similarly, this compound also reduced mRNA expression of inducible NO synthase (iNOS) and tumor necrosis factor-α (TNF-α), as analyzed by RT-PCR and real-time PCR. Interestingly, KTH-13-AD1 strongly diminished NF-κB-mediated luciferase activities and nuclear translocation of NF-κB family proteins. In accordance, KTH-13-AD1 suppressed the upstream signaling pathway of NF-κB activation, including IκBα, IKKα/β, AKT, p85/PI3K, and Src in a time- and dose-dependent manner. The autophosphorylation of Src and NF-κB observed during the overexpression of Src was also suppressed by KTH-13-AD1. These results strongly suggest that KTH-13-AD1 has strong anti-inflammatory features mediated by suppression of the Src/NF-κB regulatory loop.