Recent advances in discovery and biosynthesis of natural products from myxobacteria: an overview from 2017 to 2023.

Covering: 2017.01 to 2023.11Natural products biosynthesized by myxobacteria are appealing due to their sophisticated chemical skeletons, remarkable biological activities, and intriguing biosynthetic enzymology. This review aims to systematically summarize the advances in the discovery methods, new structures, and bioactivities of myxobacterial NPs reported in the period of 2017-2023. In addition, the peculiar biosynthetic pathways of several structural families are also highlighted.

Combined Mutual Learning Net for Raman Spectral Microbial Strain Identification.

Infectious diseases pose a significant threat to global health, yet traditional microbiological identification methods suffer from drawbacks, such as high costs and long processing times. Raman spectroscopy, a label-free and noninvasive technique, provides rich chemical information and has tremendous potential in fast microbial diagnoses. Here, we propose a novel Combined Mutual Learning Net that precisely identifies microbial subspecies. It demonstrated an average identification accuracy of 87.96% in an open-access data set with thirty microbial strains, representing a 5.76% improvement. 50% of the microbial subspecies accuracies were elevated by 1% to 46%, especially for E. coli 2 improved from 31% to 77%. Furthermore, it achieved a remarkable subspecies accuracy of 92.4% in the custom-built fiber-optical tweezers Raman spectroscopy system, which collects Raman spectra at a single-cell level. This advancement demonstrates the effectiveness of this method in microbial subspecies identification, offering a promising solution for microbiology diagnosis.

Deletion of Impdh2 in adipocyte precursors limits the expansion of white adipose tissue and enhances metabolic health with overnutrition.

The equilibrium between the hypertrophic growth of existing adipocytes and adipogenesis is vital in managing metabolic stability in white adipocytes when faced with overnutrition. Adipogenesis has been established as a key player in combating metabolic irregularities caused by various factors. However, the benefits of increasing adipogenesis-mediated white adipose tissue (WAT) expansion for metabolic health regulation remain uncertain. Our findings reveal an increase in Impdh2 expression during the adipogenesis phase, both in vivo and in vitro. Xmp enhances adipogenic potential by fostering mitotic clonal expansion (MCE). The conditional knockout of Impdh2 in adipocyte progenitor cells(APCs) in adult and aged mice effectively curbs white adipose tissue expansion, ameliorates glucose tolerance, and augments energy expenditure under high-fat diet (HFD). However, no significant difference is observed under normal chow diet (NCD). Concurrently, the knockout of Impdh2 in APCs significantly reduces the count of new adipocytes induced by HFD, without affecting adipocyte size. Mechanistically, Impdh2 regulates the proliferation of APCs during the MCE phase via Xmp. Exogenous Xmp can significantly offset the reduction in adipogenic abilities of APCs due to Impdh2 deficiency. In summary, we discovered that adipogenesis-mediated WAT expansion, induced by overnutrition, also contributes to metabolic abnormalities. Moreover, the pivotal role of Impdh2 in regulating adipogenesis in APCs offers a novel therapeutic approach to combat obesity.

Water-Capture Filter Paper Separator Realizing Ambient Li-Air Battery.

Lithium-air battery (LAB) is regarded as one of the most promising energy storage systems. However, the challenges arising from the lithium metal anode have significantly impeded the progress of LAB development. In this study, cellulose-based filter paper (FP) is utilized as a separator for ambient Li-air batteries to suppress dendrite growth and prevent H2O crossover. Thermogravimetric analysis and molecular spectrum reveal that FP enables ambient Li-air battery operation due to its surface functional groups derived from cellulose. The oxygen-enriched surface of cellulose not only enhances ion conductivity but also captures water and confines solvent molecules, thereby mitigating anode corrosion and side reactions. Compared with commercial glassfiber (GF) separator, this cellulose-based FP separator is cheaper, renewable, and environmentally friendly. Moreover, it requires less electrolyte while achieving prolonged and stable cycle life under real air environment conditions. This work presents a novel approach to realizing practical Li-air batteries by capturing water on the separator's surface. It also provides insights into the exploration and design of separators for enabling practical Li-air batteries toward their commercialization.

Timing of Cognitive Test Score Decline Prior to Incident Dementia Diagnosis in Blacks and Whites: The Atherosclerosis Risk in Communities Neurocognitive Study.

INTRODUCTION: Commonly occurring dementias include those of Alzheimer's, vascular, and mixtures of these and other pathologies. They are believed to evolve over many years, but that time interval has been difficult to establish. Our objective was to determine how many years in advance of a dementia diagnosis cognitive scores begin to change. METHODS: 14,086 dementia-free ARIC participants underwent a cognitive exam at baseline visit 2 (1990-1992, mean age 57 ± 5.72), and 11,244 at visit 4 (1996-1998), 5,640 at visit 5 (2011-2013), and 3,574 at visit 6 (2016-2017) with surveillance for dementias of all-causes combined. Within 5-year intervals after each visit, we compared performance on the Delayed Word Recall Test (DWRT), the Digit Symbol Substitution Test (DSST), the Word Fluency Test (WFT), and the combined mean of three cognitive tests at baseline in participants who were diagnosed with dementia within each interval versus those who survived the interval without a dementia diagnosis. Z-scores were adjusted for demographics and education in separate regression models for each visit. We plotted adjusted z-score means by time interval following each visit. RESULTS: During follow-up 3,334, 2,821, 1,218, and 329 dementia cases were ascertained after visits 2, 4, 5, and 6, respectively. Adjusted DWRT z-scores were significantly lower 20-25 years before dementia than those who did not experience dementia within 25 years. DSST z-scores were significantly lower at 25-30 years and 3-test combination z-scores were significantly lower as early as 30-31 years before onset. The difference between dementia and non-dementia group in the visit 2 3-test combination z-score was -0.20 at 30-31 years prior to dementia diagnosis. As expected, differences between the dementia and non-dementia groups increased closer to the time of dementia occurrence, up to their widest point at 0-5 years prior to dementia diagnosis. The difference between dementia and non-dementia groups in the visit 2 3-test combination z-score at 0-5 years was -0.90. WFT z-score differences were smaller than for the DSST or DWRT and began later. Patterns were similar in Black and White participants. CONCLUSION: DWRT, DSST, and combined 3-test z-scores were significantly lower more than 20 years prior to diagnosis in the dementia group versus the non-dementia group. Findings contribute to our knowledge of the long prodromal period in Blacks and Whites.

Advances in Novel Biomaterial-Based Strategies for Spinal Cord Injury Treatment.

Spinal cord injury (SCI) is a highly disabling neurological disorder. Its pathological process comprises an initial acute injury phase (primary injury) and a secondary injury phase (subsequent chronic injury). Although surgical, drug, and cell therapies have made some progress in treating SCI, there is no exact therapeutic strategy for treating SCI and promoting nerve regeneration due to the complexity of the pathological SCI process. The development of novel drug delivery systems to treat SCI is expected to significantly impact the individualized treatment of SCI due to its unique and excellent properties, such as active targeting and controlled release. In this review, we first describe the pathological progression of the SCI response, including primary and secondary injuries. Next, we provide a concise overview of newly developed nanoplatforms and their potential application in regulating and treating different pathological processes of SCI. Then, we introduce the existing potential problems and future clinical application perspectives of biomedical engineering-based therapies for SCI.

Advances in Nanoplatforms for Immunotherapy Applications Targeting the Tumor Microenvironment.

Cancer immunotherapy is a treatment method that activates or enhances the autoimmune response of the body to fight tumor growth and metastasis, has fewer toxic side effects and a longer-lasting efficacy than radiotherapy and chemotherapy, and has become an important means for the clinical treatment of cancer. However, clinical results from immunotherapy have shown that most patients lack responsiveness to immunotherapy and cannot benefit from this treatment strategy. The tumor microenvironment (TME) plays a critical role in the response to immunotherapy. The TME typically prevents effective lymphocyte activation, reducing their infiltration, and inhibiting the infiltration of effector T cells. According to the characteristic differences between the TME and normal tissues, various nanoplatforms with TME targeting and regulation properties have been developed for more precise regulation of the TME and have the ability to codeliver a variety of active pharmaceutical ingredients, thereby reducing systemic toxicity and improving the therapeutic effect of antitumor. In addition, the precise structural design of the nanoplatform can integrate specific functional motifs, such as surface-targeted ligands, degradable backbones, and TME stimulus-responsive components, into nanomedicines, thereby reshaping the tumor microenvironment, improving the body's immunosuppressive state, and enhancing the permeability of drugs in tumor tissues, in order to achieve controlled and stimulus-triggered release of load cargo. In this review, the physiological characteristics of the TME and the latest research regarding the application of TME-regulated nanoplatforms in improving antitumor immunotherapy will be described. Furthermore, the existing problems and further applications perspectives of TME-regulated platforms for cancer immunotherapy will also be discussed.

The Development and Application of KinomePro-DL: A Deep Learning Based Online Small Molecule Kinome Selectivity Profiling Prediction Platform.

Characterizing the kinome selectivity profiles of kinase inhibitors is essential in the early stages of novel small-molecule drug discovery. This characterization is critical for interpreting potential adverse events caused by off-target polypharmacology effects and provides unique pharmacological insights for drug repurposing development of existing kinase inhibitor drugs. However, experimental profiling of whole kinome selectivity is still time-consuming and resource-demanding. Here, we report a deep learning classification model using an in-house built data set of inhibitors against 191 well-representative kinases constructed based on a novel strategy by systematically cleaning and integrating six public data sets. This model, a multitask deep neural network, predicts the kinome selectivity profiles of compounds with novel structures. The model demonstrates excellent predictive performance, with auROC, prc-AUC, Accuracy, and Binary_cross_entropy of 0.95, 0.92, 0.90, and 0.37, respectively. It also performs well in a priori testing for inhibitors targeting different categories of proteins from internal compound collections, significantly improving over similar models on data sets from practical application scenarios. Integrated to subsequent machine learning-enhanced virtual screening workflow, novel CDK2 kinase inhibitors with potent kinase inhibitory activity and excellent kinome selectivity profiles are successfully identified. Additionally, we developed a free online web server, KinomePro-DL, to predict the kinome selectivity profiles and kinome-wide polypharmacology effects of small molecules (available on kinomepro-dl.pharmablock.com). Uniquely, our model allows users to quickly fine-tune it with their own training data sets, enhancing both prediction accuracy and robustness.

Injectable Bombyx mori (B. mori) silk fibroin/MXene conductive hydrogel for electrically stimulating neural stem cells into neurons for treating brain damage.

Brain damage is a common tissue damage caused by trauma or diseases, which can be life-threatening. Stem cell implantation is an emerging strategy treating brain damage. The stem cell is commonly embedded in a matrix material for implantation, which protects stem cell and induces cell differentiation. Cell differentiation induction by this material is decisive in the effectiveness of this treatment strategy. In this work, we present an injectable fibroin/MXene conductive hydrogel as stem cell carrier, which further enables in-vivo electrical stimulation upon stem cells implanted into damaged brain tissue. Cell differentiation characterization of stem cell showed high effectiveness of electrical stimulation in this system, which is comparable to pure conductive membrane. Axon growth density of the newly differentiated neurons increased by 290% and axon length by 320%. In addition, unfavored astrocyte differentiation is minimized. The therapeutic effect of this system is proved through traumatic brain injury model on rats. Combined with in vivo electrical stimulation, cavities formation is reduced after traumatic brain injury, and rat motor function recovery is significantly promoted.

Risk factor analysis and risk prediction study of obesity in steelworkers: model development based on an occupational health examination cohort dataset.

BACKGROUND: Obesity is increasingly recognized as a grave public health concern globally. It is associated with prevalent diseases including coronary heart disease, fatty liver, type 2 diabetes, and dyslipidemia. Prior research has identified demographic, socioeconomic, lifestyle, and genetic factors as contributors to obesity. Nevertheless, the influence of occupational risk factors on obesity among workers remains under-explored. Investigating risk factors specific to steelworkers is crucial for early detection, prediction, and effective intervention, thereby safeguarding their health. METHODS: This research utilized a cohort study examining health impacts on workers in an iron and steel company in Hebei Province, China. The study involved 5469 participants. By univariate analysis, multifactor analysis, and review of relevant literature, predictor variables were found. Three predictive models-XG Boost, Support Vector Machine (SVM), and Random Forest (RF)-were employed. RESULTS: Univariate analysis and cox proportional hazard regression modeling identified age, gender, smoking and drinking habits, dietary score, physical activity, shift work, exposure to high temperatures, occupational stress, and carbon monoxide exposure as key factors in the development of obesity in steelworkers. Test results indicated accuracies of 0.819, 0.868, and 0.872 for XG Boost, SVM, and RF respectively. Precision rates were 0.571, 0.696, and 0.765, while recall rates were 0.333, 0.592, and 0.481. The models achieved AUCs of 0.849, 0.908, and 0.912, with Brier scores of 0.128, 0.105, and 0.104, log losses of 0.409, 0.349, and 0.345, and calibration-in-the-large of 0.058, 0.054, and 0.051, respectively. Among these, the Random Forest model demonstrated superior performance. CONCLUSIONS: The research indicates that obesity in steelworkers results from a combination of occupational and lifestyle factors. Of the models tested, the Random Forest model exhibited superior predictive ability, highlighting its significant practical application.

The relationship between social frailty and loneliness in community-dwelling older adults: a cross-sectional study.

BACKGROUND: Social frailty (SF) is associated with multiple adverse health outcomes, yet there has been an inadequate focus on social frailty. The convoy model portrays the social networks through the perspective of the life course, thus providing a framework to explain the occurrence of social frailty. This study aimd to figure out the prevalence of social frailty and loneliness among community-dwelling older adults and to explore their correlations based on convoy model. METHODS: This was a cross-sectional study, and 295 older adults from 10 communities of Zhengzhou in Henan Province participated in the study. Social frailty and loneliness were assessed separately with the Social Frailty Scale and University of California at Los Angeles-Loneliness Scale. The scores of social frailty of the older adults in different characteristic communities were compared by independent sample t-test and single factor analysis of variance. The influencing factors of social frailty were analysed by multiple stepwise linear regression and the structural equation model. The correlation between social frailty and loneliness was analysed by Pearson correlation analysis. RESULTS: The total scores of social frailty and loneliness of the older adults in the community were (2.09 ± 1.53) and (43.19 ± 8.91), respectively. There was a moderate positive correlation between social frailty and loneliness (r = 0.621, P < 0.01). The results of multiple stepwise linear regression analysis showed that age, living styles, balance of payments, and loneliness were the main influencing factors of the social frailty of older adults in the community (F = 27.180, P < 0.001). The structural equation model of social frailty fitted well (χ2 = 47.292, df = 26, χ2/df = 1.819, P = 0.007; RMSEA = 0.053, 95%CI (0.028, 0.076), P = 0.359; GFI = 0.971; AGFI = 0.939; NFI = 0.904; IFI = 0.955; TLI = 0.918; CFI = 0.953; SRMR = 0.0466). CONCLUSIONS: The convoy model had certain applicability in explanation of the relationship between loneliness and social frailty among older adults in community. The incidence of social frailty among the older adults in the community was high, and loneliness was at a medium level. It is necessary to strengthen the intervention of social frailty and loneliness of the older adults in the community, improve the quality of life of the older adults, and promote the development of healthy aging.

Association between dust exposure and lung function levels in steelworkers: mediation analysis of inflammatory biomarkers.

PURPOSE: This investigation aimed to examine the mediating effect of inflammatory biomarkers on the relationship between dust exposure and lung function levels among steelworkers. METHODS: The study comprised 2,315 front-line workers employed at an iron and steel company in Tangshan, who underwent occupational health assessments through cluster sampling. Demographic and lifestyle data were collected via a self-administered questionnaire, while physical examinations measured parameters such as height and weight. Lung function was assessed using a portable pulmonary function tester (CHEST). Blood cell counts were uniformly analyzed using a Mindray fully automated biochemistry analyzer (BS-800). Inflammatory biomarkers, including leukocyte count, neutrophil count, lymphocyte count, and platelet count, were assessed, and the neutrophil-to-lymphocyte ratio and systemic immune inflammation index were computed. Generalized linear models and Spearman rank correlation analyses were employed to explore the interplay among dust exposure, inflammatory biomarkers, and alterations in lung function. A mediation analysis model was constructed to elucidate how inflammatory biomarkers mediate the relationship between dust exposure and lung function levels. RESULTS: After adjusting for covariates, dust exposure was significantly associated with reduced lung function levels, with statistically significant differences observed between dust-exposed and non-exposed groups across various lung function indicators (P < 0.001). In the dust-exposed group, inflammatory biomarkers were elevated, showing significant correlations with FVC and FEV1 (P < 0.05). However, the correlation between FEV1/FVC and various inflammatory biomarkers was insignificant (P > 0.05). Mediation analysis revealed that white blood cells and neutrophils partially mediated the association between dust exposure and FVC, with proportions of 1.75% and 1.09%, respectively. Similarly, white blood cells, neutrophils, and the systemic immune inflammation index partially mediated the association between dust exposure and FEV1, with proportions of 1.15%, 0.82%, and 0.82%, respectively. CONCLUSION: In conclusion, dust exposure poses a risk for decreased lung function levels. Inflammatory biomarkers derived from blood cells offer a valuable and easily obtainable means of identifying changes in lungfunction levels. Among these biomarkers, white blood cells, neutrophils, and the systemic immune inflammation index significantly mediate the association between dust exposure and lung function levels, although further exploration is needed to understand their underlying mechanisms.

Retrospective Analysis of Adverse Factors Impacting Medicine Logistics.

Background: Medicine logistics, particularly cryogenic storage, maintains pharmaceutical efficacy and safety. Ensuring seamless transportation and storage prevents spoilage, degradation, or contamination, safeguarding patient health. Objective: This study aimed to analyze the relationships among the components of the medication cold chain logistics system using grey relational analysis (GRA). Additionally, we utilized GRA to construct an adjacency matrix, facilitating a comprehensive understanding of the interdependencies within the system. Methods: Data from pertinent indices spanning 2021 and 2022 were utilized to conduct a quantitative analysis using GRA. This analysis aimed to identify the most influential elements affecting the growth of pharmaceutical cold chain logistics in a specific location. The negative aspects of the medication cold chain logistics system in particular areas were examined by assessing the grey relationship grades between various components and the medicine cold chain logistics system in those regions. Results: The analysis revealed significant insights into the correlated risk factors impacting medicine logistics operations. Through an examination of the financial status and operational processes of medicine logistics assets, four categories of risks were identified, encompassing transportation, storage, distribution, and quality management. These categories were established by analyzing the most significant risk factors across these operational domains. Additionally, GRA was employed to assess the factors influencing medicine logistics. The study found a strong relationship between key parameters, such as transportation risk and site facilities and equipment, and the growth of the pharmaceutical logistics sector. Operation risk emerged as the least influential factor, while site facilities and equipment, transportation risk, and operation risk demonstrated substantial influence on the region's medical logistics sector growth. Conclusion: This study provides important recommendations to improve medicine logistics, aiming to mitigate adverse effects and elevate inventory management. Implementation can enhance efficiency and safety in the medicine supply chain, benefiting patient care and public health.

Ultrasensitive dynamic light scattering immunodetection of alpha-fetoprotein using heptamer-amplified nanoparticle crosslinking aggregation.

A novel construction strategy is introduced for an ultrasensitive dynamic light scattering (DLS) immunosensor targeting alpha fetoprotein (AFP). This approach relies on a self-assembled heptamer fusion protein (A1-C4bpα), incorporating the dual functions of multivalent recognition and crosslinking aggregation amplification due to the presence of seven AFP-specific A1 nanobodies on the A1-C4bpα heptamer. Leveraging antibody-functionalized magnetic nanoparticles for target AFP capture and DLS signal output, the proposed heptamer-assisted DLS immunosensor offers high sensitivity, strong specificity, and ease of operation. Under the optimized conditions, the designed DLS immunosensor demonstrates excellent linear detection of AFP in the concentration range 0.06 ng mL-1 to 512 ng mL-1, with a detection limit of 15 pg mL-1. The selectivity, accuracy, precision, practicability, and reliability of this newly developed method were further validated through an assay of AFP levels in spiked and actual human serum samples. This work introduces a novel approach for constructing ultrasensitive DLS immunosensors, easily extendable to the sensitive determination of other targets via simply replacing the nanobody sequence, holding great promise in various applications, particularly in disease diagnosis.

Ultrasound-electrochemistry assisted liquid-phase co-exfoliation of phosphorene decorated by Au-Ag bimetallic nanoparticles as nanozyme for smartphone-based portable sensing of 4-nitrophenol.

The stability of black phosphorene (BP) and its preparation and modification for developing and applying devices have become a hot topic in the interdisciplinary field. We propose ultrasound-electrochemistry co-assisted liquid-phase exfoliation as an eco-friendly one-step method to prepare gold-silver bimetallic nanoparticles (Au-AgNPs)-decorated BP nanozyme for smartphone-based portable sensing of 4-nitrophenol (4-NP) in different water sources. The structure, morphology, composition, and properties of Au-AgNPs-BP nanozyme are characterized by multiple instrumental analyses. Bimetallic salts are induced to efficiently occupy oxidative sites of BP to form highly stable Au-AgNPs-BP nanozyme and guarantee the integrity of the lamellar BP. The electrochemistry shortens the exfoliation time of the BP nanosheet and contributes to the loading efficiency of bimetallic nanoparticles on the BP nanosheet. Au-AgNPs-BP-modified screen-printed carbon electrode coupled with palm-sized smartphone-controlled wireless electrochemical analyzer as a portable wireless intelligent sensing platform was applied to the determination of 4-NP in a linear range of 0.6-10 µM with a limit of detection of 63 nM. It enables on-site determination of 4-NP content in lake water, river water, and irrigation ditch water. This work will provide a reference for an eco-friendly one-step preparation of bimetallic nanoparticle-decorated graphene-like materials as nanozymes and their smartphone-based portable sensing application outdoors.

How to obtain product green design requirements based on sentiment analysis and topic analysis: Using washing machine online reviews as an example.

Green design involves the entire life cycle of a product, including stages such as raw material acquisition, production and manufacturing, sales and transportation, use, recycling, and disposal. Extracting customer requirements (CRs) related to product green design (PGD) is one of the necessary conditions for achieving the dual carbon goal. However, only a few studies have evaluated CRs for PGD from a full life cycle perspective. This study obtained 20,000 online reviews of washing machines from e-commerce platforms. The customers' sentiment tendencies toward the requirements of washing machines at various stages of their life cycle are analyzed and evaluated. The CRs contained in online washing machine reviews were identified through cluster analysis. Based on the life cycle theory, the product green design requirements (PGDRs) of CRs were extracted and analyzed. This study can provide theoretical and methodological support for green product design.

Performance evaluation of deep learning image reconstruction algorithm for dual-energy spectral CT imaging: A phantom study.

OBJECTIVES: To evaluate the performance of deep learning image reconstruction (DLIR) algorithm in dual-energy spectral CT (DEsCT) as a function of radiation dose and image energy level, in comparison with filtered-back-projection (FBP) and adaptive statistical iterative reconstruction-V (ASIR-V) algorithms. METHODS: An ACR464 phantom was scanned with DEsCT at four dose levels (3.5 mGy, 5 mGy, 7.5 mGy, and 10 mGy). Virtual monochromatic images were reconstructed at five energy levels (40 keV, 50 keV, 68 keV, 74 keV, and 140 keV) using FBP, 50% and 100% ASIR-V, DLIR at low (DLIR-L), medium (DLIR-M), and high (DLIR-H) settings. The noise power spectrum (NPS), task-based transfer function (TTF) and detectability index (d') were computed and compared among reconstructions. RESULTS: NPS area and noise increased as keV decreased, with DLIR having slower increase than FBP and ASIR-V, and DLIR-H having the lowest values. DLIR had the best 40 keV/140 keV noise ratio at various energy levels, DLIR showed higher TTF (50%) than ASIR-V for all materials, especially for the soft tissue-like polystyrene insert, and DLIR-M and DLIR-H provided higher d' than DLIR-L, ASIR-V and FBP in all dose and energy levels. As keV increases, d' increased for acrylic insert, and d' of the 50 keV DLIR-M and DLIR-H images at 3.5 mGy (7.39 and 8.79, respectively) were higher than that (7.20) of the 50 keV ASIR-V50% images at 10 mGy. CONCLUSIONS: DLIR provides better noise containment for low keV images in DEsCT and higher TTF(50%) for the polystyrene insert over ASIR-V. DLIR-H has the lowest image noise and highest detectability in all dose and energy levels. DEsCT 50 keV images with DLIR-M and DLIR-H show potential for 65% dose reduction over ASIR-V50% withhigher d'.

Prediabetes, intervening diabetes and subsequent risk of dementia: the Atherosclerosis Risk in Communities (ARIC) study.

AIMS/HYPOTHESIS: The aim of this work was to evaluate whether the association of prediabetes with dementia is explained by the intervening onset of diabetes. METHODS: Among participants of the Atherosclerosis Risk in Communities (ARIC) study we defined baseline prediabetes as HbA1c 39-46 mmol/mol (5.7-6.4%) and subsequent incident diabetes as a self-reported physician diagnosis or use of diabetes medication. Incident dementia was ascertained via active surveillance and adjudicated. We quantified the association of prediabetes with dementia risk before and after accounting for the subsequent development of diabetes among ARIC participants without diabetes at baseline (1990-1992; participants aged 46-70 years). We also evaluated whether age at diabetes diagnosis modified the risk of dementia. RESULTS: Among 11,656 participants without diabetes at baseline, 2330 (20.0%) had prediabetes. Before accounting for incident diabetes, prediabetes was significantly associated with the risk of dementia (HR 1.12 [95% CI 1.01, 1.24]). After accounting for incident diabetes, the association was attenuated and non-significant (HR 1.05 [95% CI 0.94, 1.16]). Earlier age of onset of diabetes had the strongest association with dementia: HR 2.92 (95% CI 2.06, 4.14) for onset before 60 years; HR 1.73 (95% CI 1.47, 2.04) for onset at 60-69 years; and HR 1.23 (95% CI 1.08, 1.40) for onset at 70-79 years. CONCLUSIONS/INTERPRETATION: Prediabetes is associated with dementia risk but this risk is explained by the subsequent development of diabetes. Earlier age of onset of diabetes substantially increases dementia risk. Preventing or delaying progression of prediabetes to diabetes will reduce dementia burden.

Constructing a Myxobacterial Natural Product Database to Facilitate NMR-Based Metabolomics Bioprospecting of Myxobacteria.

Myxobacteria are fascinating prokaryotes featuring a potent capacity for producing a wealth of bioactive molecules with intricate chemical topology as well as intriguing enzymology, and thus it is critical to developing an efficient pipeline for bioprospecting. Herein, we construct the database MyxoDB, the first public compendium solely dedicated to myxobacteria, which enabled us to provide an overview of the structural diversity and taxonomic distribution of known myxobacterial natural products. Moreover, we demonstrated that the cutting-edge NMR-based metabolomics was effective to differentiate the biosynthetic priority of myxobacteria, whereby MyxoDB could greatly streamline the dereplication of multifarious known compounds and accordingly speed up the discovery of new compounds. This led to the rapid identification of a class of linear di-lipopeptides (archangimins) and a rare rearranged sterol (corasterol) that were endowed with unique chemical architectures and/or biosynthetic enzymology. We also showcased that NMR-based metabolomics, MyxoDB, and genomics can also work concertedly to accelerate the targeted discovery of a polyketidic compound pyxipyrrolone C. All in all, this study sets the stage for the discovery of many more novel natural products from underexplored myxobacterial resources.

Low expression of the dynamic network markers FOS/JUN in pre-deteriorated epithelial cells is associated with the progression of colorectal adenoma to carcinoma.

BACKGROUND: Deterioration of normal intestinal epithelial cells is crucial for colorectal tumorigenesis. However, the process of epithelial cell deterioration and molecular networks that contribute to this process remain unclear. METHODS: Single-cell data and clinical information were downloaded from the Gene Expression Omnibus (GEO) database. We used the recently proposed dynamic network biomarker (DNB) method to identify the critical stage of epithelial cell deterioration. Data analysis and visualization were performed using R and Cytoscape software. In addition, Single-Cell rEgulatory Network Inference and Clustering (SCENIC) analysis was used to identify potential transcription factors, and CellChat analysis was conducted to evaluate possible interactions among cell populations. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set variation analysis (GSVA) analyses were also performed. RESULTS: The trajectory of epithelial cell deterioration in adenoma to carcinoma progression was delineated, and the subpopulation of pre-deteriorated epithelial cells during colorectal cancer (CRC) initialization was identified at the single-cell level. Additionally, FOS/JUN were identified as biomarkers for pre-deteriorated epithelial cell subpopulations in CRC. Notably, FOS/JUN triggered low expression of P53-regulated downstream pro-apoptotic genes and high expression of anti-apoptotic genes through suppression of P53 expression, which in turn inhibited P53-induced apoptosis. Furthermore, malignant epithelial cells contributed to the progression of pre-deteriorated epithelial cells through the GDF signaling pathway. CONCLUSIONS: We demonstrated the trajectory of epithelial cell deterioration and used DNB to characterize pre-deteriorated epithelial cells at the single-cell level. The expression of DNB-neighboring genes and cellular communication were triggered by DNB genes, which may be involved in epithelial cell deterioration. The DNB genes FOS/JUN provide new insights into early intervention in CRC.