Proinflammatory phenotype of B10 and B10pro cells elicited by TNF-α in rheumatoid arthritis.

OBJECTIVES: B10 and B10pro cells suppress immune responses via secreting interleukin (IL)-10. However, their regulators and underlying mechanisms, especially in human autoimmune diseases, are elusive. This study aimed to address these questions in rheumatoid arthritis (RA), one of the most common highly disabling autoimmune diseases. METHODS: The frequencies and functions of B10 and B10pro cells in healthy individuals and patients with RA were first analysed. The effects of proinflammatory cytokines, particularly tumour necrosis factor (TNF)-α on the quantity, stability and pathogenic phenotype of these cells, were then assessed in patients with RA before and after anti-TNF therapy. The underlying mechanisms were further investigated by scRNA-seq database reanalysis, transcriptome sequencing, TNF-α-/- and B cell-specific SHIP-1-/- mouse disease model studies. RESULTS: TNF-α was a key determinant for B10 cells. TNF-α elicited the proinflammatory feature of B10 and B10pro cells by downregulating IL-10, and upregulating interferon-γ and IL-17A. In patients with RA, B10 and B10pro cells were impaired with exacerbated proinflammatory phenotype, while anti-TNF therapy potently restored their frequencies and immunosuppressive functions, consistent with the increased B10 cells in TNF-α-/- mice. Mechanistically, TNF-α diminished B10 and B10pro cells by inhibiting their glycolysis and proliferation. TNF-α also regulated the phosphatidylinositol phosphate signalling of B10 and B10pro cells and dampened the expression of SHIP-1, a dominant phosphatidylinositol phosphatase regulator of these cells. CONCLUSIONS: TNF-α provoked the proinflammatory phenotype of B10 and B10pro cells by disturbing SHIP-1 in RA, contributing to the disease development. Reinstating the immunosuppressive property of B10 and B10pro cells might represent novel therapeutic approaches for RA.

Advancing drug development in pediatric oncology, a focus on cancer biology and targeted therapies: iMATRIX platform.

With the development of novel treatment therapies as well as evolving and innovative approaches to conduct clinical trials, the landscape of pediatric oncology drug development has dramatically changed in recent years. Despite this change, approvals for new drugs and labeling updates to ensure availability of proper treatment for pediatric patients with cancer remain slow. The context of drug development in pediatric tumors has also changed with regulatory initiatives in the US and Europe, creating a great need for faster development of novel drugs. Today, conventional study designs have been replaced or complemented by novel clinical trial designs, such as master protocols and platform trials, to optimize cancer drug development and enable faster regulatory approval. The iMATRIX platform is a mechanism-of-action (MOA)-based phase 1/2 trial framework for concurrently studying multiple molecules across a range of relevant pediatric tumor types, taking into account the biology of each pediatric tumor type. Six studies have been conducted, ongoing, or planned on the iMATRIX platform - investigating atezolizumab, cobimetinib, entrectinib, idasanutlin, alectinib, and glofitamab. A brief overview of study designs and characteristics are shared in this article, along with learnings from them.

DrugSpaceX: a large screenable and synthetically tractable database extending drug space.

One of the most prominent topics in drug discovery is efficient exploration of the vast drug-like chemical space to find synthesizable and novel chemical structures with desired biological properties. To address this challenge, we created the DrugSpaceX (https://drugspacex.simm.ac.cn/) database based on expert-defined transformations of approved drug molecules. The current version of DrugSpaceX contains >100 million transformed chemical products for virtual screening, with outstanding characteristics in terms of structural novelty, diversity and large three-dimensional chemical space coverage. To illustrate its practical application in drug discovery, we used a case study of discoidin domain receptor 1 (DDR1), a kinase target implicated in fibrosis and other diseases, to show DrugSpaceX performing a quick search of initial hit compounds. Additionally, for ligand identification and optimization purposes, DrugSpaceX also provides several subsets for download, including a 10% diversity subset, an extended drug-like subset, a drug-like subset, a lead-like subset, and a fragment-like subset. In addition to chemical properties and transformation instructions, DrugSpaceX can locate the position of transformation, which will enable medicinal chemists to easily integrate strategy planning and protection design.

Gut microbiome in neuroendocrine and neuroimmune interactions: The case of genistein.

The healthy and diverse microbes living in our gut provide numerous benefits to our health. It is increasingly recognized that the gut microbiome affects the host's neurobehavioral state through production of metabolites, modulation of intestinal immunity (e.g., cytokines) and other mechanisms (e.g., gut neuropeptides). By sending the sensed information (e.g., metabolic and immunologic mediators) about the state of the inner organs to the brain via afferent fibers, the vagus nerve maintains one of the connections between the brain and GI tract, and oversees many critical bodily functions (e.g., mood, immune response, digestion and heart rate). The microbiota-gut-brain axis is a bidirectional communication between the gut, its microbiome, and the nervous system. In the present review, the roles of microbiome in neuroendocrine and neuroimmune interactions have been discussed using naturally occurring isoflavones, particularly the phytoestrogen genistein, as there are sex differences in the interactions among the microbiome, hormones, immunity and disease susceptibility. A deep understanding of the mechanisms underlying the interactions among the endocrine modulators, brain, endocrine glands, gut immune cells, vagus nerve, enteric nervous system and gut microbiome will provide important knowledges that may ultimately lead to treatment and prevention of debilitating disorders characterized by deficits of microbiome-neuroendocrine-neuroimmune relationships.

SHIP-1 Deficiency in AID+ B Cells Leads to the Impaired Function of B10 Cells with Spontaneous Autoimmunity.

Unlike conventional B cells, regulatory B cells exhibit immunosuppressive functions to downregulate inflammation via IL-10 production. However, the molecular mechanism regulating the production of IL-10 is not fully understood. In this study, we report the finding that activation-induced cytidine deaminase (AID) is highly upregulated in the IL-10-competent B cell (B10) cell from Innp5dfl/flAicdaCre/+ mice, whereas the 5' inositol phosphatase SHIP-1 is downregulated. Notably, SHIP-1 deficiency in AID+ B cells leads to a reduction in cell count and impaired IL-10 production by B10 cells. Furthermore, the Innp5dfl/flAicdaCre/+ mouse model shows B cell-dependent autoimmune lupus-like phenotypes, such as elevated IgG serum Abs, formation of spontaneous germinal centers, production of anti-dsDNA and anti-nuclear Abs, and the obvious deposition of IgG immune complexes in the kidney with age. We observe that these lupus-like phenotypes can be reversed by the adoptive transfer of B10 cells from control Innp5dfl/fl mice, but not from the Innp5dfl/flAicdaCre/+ mice. This finding highlights the importance of defective B10 cells in Innp5dfl/flAicdaCre/+ mice. Whereas p-Akt is significantly upregulated, MAPK and AP-1 activation is impaired in B10 cells from Innp5dfl/flAicdaCre/+ mice, resulting in the reduced production of IL-10. These results show that SHIP-1 is required for the maintenance of B10 cells and production of IL-10, and collectively suggests that SHIP-1 could be a new potential therapeutic target for the treatment of autoimmune diseases.

The costs of dementia subtypes to California Medicare fee-for-service, 2015.

INTRODUCTION: Dementia is among the costliest of medical conditions, but it is not known how these costs vary by dementia subtype. METHODS: The effect of dementia diagnosis subtype on direct health care costs and utilization was estimated using 2015 California Medicare fee-for-service data. Potential drivers of increased costs in Lewy body dementia (LBD), in comparison to Alzheimer's disease, were tested. RESULTS: 3,001,987 Medicare beneficiaries were identified, of which 8.2% had a dementia diagnosis. Unspecified dementia was the most common diagnostic category (59.6%), followed by Alzheimer's disease (23.2%). LBD was the costliest subtype to Medicare, on average, followed by vascular dementia. The higher costs in LBD were explained in part by falls, urinary incontinence or infection, depression, anxiety, dehydration, and delirium. DISCUSSION: Dementia subtype is an important predictor of health care costs. Earlier identification and targeted treatment might mitigate the costs associated with co-occurring conditions in LBD.

Progression from normal cognition to mild cognitive impairment in a diverse clinic-based and community-based elderly cohort.

INTRODUCTION: Investigation of the conversion rates from normal cognition (NC) to mild cognitive impairment (MCI) is important, as effective early intervention could potentially prevent or substantially delay the onset of dementia. However, reported conversion rates differ across studies and recruitment source. Our study examined predictors of conversion from NC to MCI in a racially and ethnically diverse sample drawn both from community and clinic recruitment sources. METHODS: Rates and predictors of conversion were assessed in an ongoing prospective longitudinal study at University of California, Davis, Alzheimer's Disease Center from 2000 to 2015. Participants (n = 254) were recruited through a clinic (5%) and community sample (95%). They were clinically confirmed as cognitively normal at baseline and followed up to seven years. Recruitment source, demographic factors (age, gender, race/ethnicity, year of education, APOE ε4 positive), cognitive measures (SENAS test scores), functional assessments (CDR sum of boxes), and neuroimaging measures (total brain volume, total hippocampal volume, white hyperintensity volume) were assessed as predictors of conversion from cognitively normal to mild cognitive impairment using proportional hazards models. RESULTS: Of 254 participants, 62 (11 clinic, 51 community) progressed to MCI. The clinic-based sample showed an annual conversion rate of 30% (95% CI 17%-54%) per person-year, whereas the community-based sample showed a conversion rate of 5% (95% CI 3%-6%) per person-year. Risk factors for conversion include clinic-based recruitment, being older, lower executive function and worse functional assessment at baseline, and smaller total brain volume. DISCUSSION: Older adults who sought out a clinical evaluation, even when they are found to have normal cognition, have increased risk of subsequent development of MCI. Results are consistent with other studies showing subjective cognitive complaints are a risk for future cognitive impairment, but extend such findings to show that those who seek evaluation for their complaints are at particularly high risk. Moreover, these individuals have subtle, but significant differences in functional and cognitive abilities that, in the presence of concerns and evidence of atrophy on by brain imaging, warrant continued clinical follow-up. These risk factors could also be used as stratification variables for dementia prevention clinical trial design.

Underutilization of gene expression profiling for early-stage breast cancer in California.

PURPOSE: To describe the utilization of gene expression profiling (GEP) among California breast cancer patients, identify predictors of use of GEP, and evaluate how utilization of GEP influenced treatment of early-stage breast cancer. METHODS: All women diagnosed with hormone-receptor-positive, node-negative breast cancer reported to the California Cancer Registry between January 2008 and December 2010 were linked to Oncotype DX (ODX) assay results. RESULTS: Overall, 26.7 % of 23,789 eligible patients underwent the assay during the study period. Women age 65 or older were much less likely than women under age 50 to be tested (15.1 vs. 41.4 %, p < 0.001). Black women were slightly less likely and Asian women were slightly more likely than non-Hispanic white women to undergo GEP with the ODX assay (22.2 and 28.9 vs. 26.9 %, respectively, p < 0.001). Patients residing in low SES census tracts had the lowest use of the test (8.9 %), with the proportion increasing with higher SES category. Women with Medicaid health insurance were less likely than other women to be tested (17.7 vs. 27.5 %, p < 0.001). Receipt of adjuvant chemotherapy (ACT) was associated with the ODX recurrence score, although only 63 % of patients whose recurrence scores indicated a high benefit received ACT. Of patients not tested, 15 % received ACT. CONCLUSIONS: Nearly three-fourths of eligible breast cancer patients in California during the 3-year period 2008 through 2010 did not undergo GEP. As a result, it is likely that many women unnecessarily received ACT and suffered associated morbidity. In addition, some high-risk women who would have benefited most from ACT were not identified.

TCDD modulation of gut microbiome correlated with liver and immune toxicity in streptozotocin (STZ)-induced hyperglycemic mice.

An increasing body of evidence has shown the important role of the gut microbiome in mediating toxicity following environmental contaminant exposure. The goal of this study was to determine if the adverse metabolic effects of chronic 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) exposure would be sufficient to exacerbate hyperglycemia, and to further determine if these outcomes were attributable to the gut microbiota alteration. Adult male CD-1 mice were exposed to TCDD (6µg/kg body weight biweekly) by gavage and injected (i.p.) with STZ (4×50mg/kg body weight) to induced hyperglycemia. 16S rRNA sequencing was used to characterize the changes in the microbiome community composition. Glucose monitoring, flow cytometry, histopathology, and organ characterization were performed to determine the deleterious phenotypic changes of TCDD exposure. Chronic TCDD treatment did not appear to exacerbate STZ-induced hyperglycemia as blood glucose levels were slightly reduced in the TCDD treated mice; however, polydipsia and polyphagia were observed. Importantly, TCDD exposure caused a dramatic change in microbiota structure, as characterized at the phylum level by increasing Firmicutes and decreasing Bacteroidetes while at the family level most notably by increasing Lactobacillaceae and Desulfovibrionaceae, and decreasing Prevotellaceae and ACK M1. The changes in microbiota were further found to be broadly associated with phenotypic changes seen from chronic TCDD treatment. In particular, the phylum level Bacteroidetes to Firmicutes ratio negatively correlated with both liver weight and liver pathology, and positively associated with %CD3(+)NK(+) T cells, a key mediator of host-microbial interactions. Collectively, these findings suggest that the dysregulated gut microbiome may contribute to the deleterious effects (e.g., liver toxicity) seen with TCDD exposure.

Multi-phase features interaction transformer network for liver tumor segmentation and microvascular invasion assessment in contrast-enhanced CT.

Precise segmentation of liver tumors from computed tomography (CT) scans is a prerequisite step in various clinical applications. Multi-phase CT imaging enhances tumor characterization, thereby assisting radiologists in accurate identification. However, existing automatic liver tumor segmentation models did not fully exploit multi-phase information and lacked the capability to capture global information. In this study, we developed a pioneering multi-phase feature interaction Transformer network (MI-TransSeg) for accurate liver tumor segmentation and a subsequent microvascular invasion (MVI) assessment in contrast-enhanced CT images. In the proposed network, an efficient multi-phase features interaction module was introduced to enable bi-directional feature interaction among multiple phases, thus maximally exploiting the available multi-phase information. To enhance the model's capability to extract global information, a hierarchical transformer-based encoder and decoder architecture was designed. Importantly, we devised a multi-resolution scales feature aggregation strategy (MSFA) to optimize the parameters and performance of the proposed model. Subsequent to segmentation, the liver tumor masks generated by MI-TransSeg were applied to extract radiomic features for the clinical applications of the MVI assessment. With Institutional Review Board (IRB) approval, a clinical multi-phase contrast-enhanced CT abdominal dataset was collected that included 164 patients with liver tumors. The experimental results demonstrated that the proposed MI-TransSeg was superior to various state-of-the-art methods. Additionally, we found that the tumor mask predicted by our method showed promising potential in the assessment of microvascular invasion. In conclusion, MI-TransSeg presents an innovative paradigm for the segmentation of complex liver tumors, thus underscoring the significance of multi-phase CT data exploitation. The proposed MI-TransSeg network has the potential to assist radiologists in diagnosing liver tumors and assessing microvascular invasion.

Angiotensin-converting enzyme 2 alleviates liver fibrosis through the renin-angiotensin system.

The present letter to the editor is related to the study titled 'Angiotensin-converting enzyme 2 improves liver fibrosis in mice by regulating autophagy of hepatic stellate cells'. Angiotensin-converting enzyme 2 can alleviate liver fibrosis by regulating autophagy of hepatic stellate cells and affecting the renin-angiotensin system.

A small intestinal bile acid modulates the gut microbiome to improve host metabolic phenotypes following bariatric surgery.

Bariatric surgical procedures such as sleeve gastrectomy (SG) provide effective type 2 diabetes (T2D) remission in human patients. Previous work demonstrated that gastrointestinal levels of the bacterial metabolite lithocholic acid (LCA) are decreased after SG in mice and humans. Here, we show that LCA worsens glucose tolerance and impairs whole-body metabolism. We also show that taurodeoxycholic acid (TDCA), which is the only bile acid whose concentration increases in the murine small intestine post-SG, suppresses the bacterial bile acid-inducible (bai) operon and production of LCA both in vitro and in vivo. Treatment of diet-induced obese mice with TDCA reduces LCA levels and leads to microbiome-dependent improvements in glucose handling. Moreover, TDCA abundance is decreased in small intestinal tissue from T2D patients. This work reveals that TDCA is an endogenous inhibitor of LCA production and suggests that TDCA may contribute to the glucoregulatory effects of bariatric surgery.

Robust scoring of selective drug responses for patient-tailored therapy selection.

Most patients with advanced malignancies are treated with severely toxic, first-line chemotherapies. Personalized treatment strategies have led to improved patient outcomes and could replace one-size-fits-all therapies, yet they need to be tailored by testing of a range of targeted drugs in primary patient cells. Most functional precision medicine studies use simple drug-response metrics, which cannot quantify the selective effects of drugs (i.e., the differential responses of cancer cells and normal cells). We developed a computational method for selective drug-sensitivity scoring (DSS), which enables normalization of the individual patient's responses against normal cell responses. The selective response scoring uses the inhibition of noncancerous cells as a proxy for potential drug toxicity, which can in turn be used to identify effective and safer treatment options. Here, we explain how to apply the selective DSS calculation for guiding precision medicine in patients with leukemia treated across three cancer centers in Europe and the USA; the generic methods are also widely applicable to other malignancies that are amenable to drug testing. The open-source and extendable R-codes provide a robust means to tailor personalized treatment strategies on the basis of increasingly available ex vivo drug-testing data from patients in real-world and clinical trial settings. We also make available drug-response profiles to 527 anticancer compounds tested in 10 healthy bone marrow samples as reference data for selective scoring and de-prioritization of drugs that show broadly toxic effects. The procedure takes <60 min and requires basic skills in R.

Can financial development improve environmental quality? New findings from spatial measures of Chinese urban panel data.

China's economy has achieved remarkable success, while it has also paid a high environmental cost. Environmental pollution not only causes great economic losses, but also severely restricts the development of society. Both theory and practice demonstrate that financial development plays a significant role in environmental governance, but the internal mechanism of its impacts has remained to be explored. It is vital to investigate the influence mechanism of financial development on the environmental quality in order to accomplish sustainable economic development through finance and enhance environmental quality concurrently. This paper, using mediating model, spatial Durbin and spatial error model, constructs a theoretical framework financial development on environmental quality from two dimensions, i.e., Financial Interrelations Ratio (FIR) and Financial Efficiency (FE), based on panel data of 234 cities in China from 2010 to 2019. And the results are as follows: (1) the improvement of Financial Interrelations Ratio (FIR) and Financial Efficiency (FE) had not yet reached the level of environmental pollution improvement; (2) in terms of mechanism, the rise of the level of financial development promoted economic growth, but inhibited the optimization of industrial structure, which increased industrial pollution emissions and deteriorates environmental quality; (3) urban environment had significant spatial dependence; (4) the impact of financial development on environmental quality in eastern region has been transformed into an improvement effect, while it still shows a deteriorating effect in central and western regions. Some policy recommendations related to the use of financial development to improve environmental quality are proposed at last, which will help to promote ecological protection and high quality synergistic economic development in China.

Six weeks effect of different nanocellulose on blood lipid level and small intestinal morphology in mice.

AIMS: Cellulose nanofibrils (CNF, or NFC), cellulose nanocrystals (CNC, or NCC), and Tempo (2,2,6,6-tetramethylpiperidine-1-oxyl radical) oxidized CNF (Tempo-CNF) were compared for the short-term effect on mice fed with a high-fat and high-sugar (Western diet, WD) to investigate their effect when combined with a sub-optimal diet. SCOPE: Thirty C57B/C female mice (10 weeks old; 5-6 mice/group) were given water, cellulose, or three types of nanocellulose once daily in a dose of 30 mg/kg body weight by oral gavage. After six weeks, weight changes, fecal output, glucose homeostasis, and gut permeability showed no significant among groups. Serum analysis including triglycerides, cholesterol and total bile acids and small intestinal morphology including villus length, villus width, crypt depth, goblet cell count and goblet cell density were no difference for all groups. Only CNC group had higher excretion of bile acids in the feces. CONCLUSIONS: These results suggest that current treated dose using three types of nanocellulose had no detrimental effects on blood lipid level and small intestinal morphology.

Chronic exposure to nanocellulose altered depression-related behaviors in mice on a western diet: The role of immune modulation and the gut microbiome.

AIMS: To determine if cellulose nanofibrils (CNF) have potential applications as food additives. MATERIALS AND METHODS: Male C57BL/6 mice on a Western diet were exposed to CNF for one month at a dose of 30 mg/kg by gavage. Male NOD mice, a model for type 1 diabetes (T1D), were used in a six-month study. KEY FINDINGS: Sequencing analysis of 16S rRNA genes suggested significant changes in gut microbiome of male C57BL/6 mice exposed to CNF. Analysis of functional metagenomics indicated that many of the functional contents that might be altered following CNF ingestion were associated with lipid and carbohydrate processing. Further studies in NOD mice suggested that there were some decreases in the blood glucose levels during the insulin tolerance test and glucose tolerance test following CNF treatment. However, these small decreases were not considered biologically meaningful as there were no significant changes in either the area under the curve or the first-order rate constant for glucose disappearance. Moreover, serum concentrations of cytokines/chemokines including IL-3, IL-12(p70) and the keratinocyte chemoattractant were increased following chronic exposure to CNF. In addition, behavioral studies suggested that the percentage of immobility time during the tail-suspension test was significantly increased following six months of exposure to CNF in NOD mice, signifying an increase in depression-related behavior. SIGNIFICANCE: Collectively, long-term CNF consumption was associated with changes in the ecology of the gut microbiome, immune homeostasis, and possibly energy metabolism and mental health in male NOD mice on a Western diet.

Repurposed Fenoprofen Targeting SaeR Attenuates Staphylococcus aureus Virulence in Implant-Associated Infections.

Implant-associated infections (IAIs) caused by S. aureus can result in serious challenges after orthopedic surgery. Due to biofilm formation and antibiotic resistance, this refractory infection is highly prevalent, and finding drugs to attenuate bacterial virulence is becoming a rational alternative strategy. In S. aureus, the SaeRS two-component system (TCS) plays a key role in the production of over 20 virulence factors and the pathogenesis of the bacterium. Here, by conducting a structure-based virtual screening against SaeR, we identified that fenoprofen, a USA Food and Drug Administration (FDA)-approved nonsteroid anti-inflammatory drug (NSAID), had excellent inhibitory potency against the response regulator SaeR protein. We showed that fenoprofen attenuated the virulence of S. aureus without drug resistance. In addition, it was helpful in relieving osteolysis and restoring the walking ability of mice in vitro and in implant-associated infection models. More importantly, fenoprofen treatment suppressed biofilm formation and changed the biofilm structure, which caused S. aureus to form loose and porous biofilms that were more vulnerable to infiltration and elimination by leukocytes. Our results reveal that fenoprofen is a potent antivirulence agent with potential value in clinical applications and that SaeR is a drug target against S. aureus implant-associated infections.

Systematic review of computational methods for drug combination prediction.

Synergistic effects between drugs are rare and highly context-dependent and patient-specific. Hence, there is a need to develop novel approaches to stratify patients for optimal therapy regimens, especially in the context of personalized design of combinatorial treatments. Computational methods enable systematic in-silico screening of combination effects, and can thereby prioritize most potent combinations for further testing, among the massive number of potential combinations. To help researchers to choose a prediction method that best fits for various real-world applications, we carried out a systematic literature review of 117 computational methods developed to date for drug combination prediction, and classified the methods in terms of their combination prediction tasks and input data requirements. Most current methods focus on prediction or classification of combination synergy, and only a few methods consider the efficacy and potential toxicity of the combinations, which are the key determinants of therapeutic success of drug treatments. Furthermore, there is a need to further develop methods that enable dose-specific predictions of combination effects across multiple doses, which is important for clinical translation of the predictions, as well as model-based identification of biomarkers predictive of heterogeneous drug combination responses. Even if most of the computational methods reviewed focus on anticancer applications, many of the modelling approaches are also applicable to antiviral and other diseases or indications.

Multi-omics profiling visualizes dynamics of cardiac development and functions.

Cardiogenesis is a tightly regulated dynamic process through a continuum of differentiation and proliferation events. Key factors and pathways governing this process remain incompletely understood. Here, we investigate mice hearts from embryonic day 10.5 to postnatal week 8 and dissect developmental changes in phosphoproteome-, proteome-, metabolome-, and transcriptome-encompassing cardiogenesis and cardiac maturation. We identify mitogen-activated protein kinases as core kinases involved in transcriptional regulation by mediating the phosphorylation of chromatin remodeling proteins during early cardiogenesis. We construct the reciprocal regulatory network of transcription factors (TFs) and identify a series of TFs controlling early cardiogenesis involved in cycling-dependent proliferation. After birth, we identify cardiac resident macrophages with high arachidonic acid metabolism activities likely involved in the clearance of injured apoptotic cardiomyocytes. Together, our comprehensive multi-omics data offer a panoramic view of cardiac development and maturation that provides a resource for further in-depth functional exploration.

Dietary advanced glycation end-products elicit toxicological effects by disrupting gut microbiome and immune homeostasis.

The aging immune system is characterized by a low-grade chronic systemic inflammatory state ("inflammaging") marked by elevated serum levels of inflammatory molecules such as interleukin (IL)-6 and C-reactive protein (CRP). These inflammatory markers were also reported to be strong predictors for the development/severity of Type 2 diabetes, obesity, and COVID-19. The levels of these markers have been positively associated with those of advanced glycation end-products (AGEs) generated via non-enzymatic glycation and oxidation of proteins and lipids during normal aging and metabolism. Based on the above observations, it is clinically important to elucidate how dietary AGEs modulate inflammation and might thus increase the risk for aging-exacerbated diseases. The present narrative review discusses the potential pro-inflammatory properties of dietary AGEs with a focus on the inflammatory mediators CRP, IL-6 and ferritin, and their relations to aging in general and Type 2 diabetes in particular. In addition, underlying mechanisms - including those related to gut microbiota and the receptors for AGEs, and the roles AGEs might play in affecting physiologies of the healthy elderly, obese individuals, and diabetics are discussed in regard to any greater susceptibility to COVID-19.