Initiating PeriCBD to probe perinatal influences on neurodevelopment during 3-10 years in China.

Adverse perinatal factors can interfere with the normal development of the brain, potentially resulting in long-term effects on the comprehensive development of children. Presently, the understanding of cognitive and neurodevelopmental processes under conditions of adverse perinatal factors is substantially limited. There is a critical need for an open resource that integrates various perinatal factors with the development of the brain and mental health to facilitate a deeper understanding of these developmental trajectories. In this Data Descriptor, we introduce a multicenter database containing information on perinatal factors that can potentially influence children's brain-mind development, namely, periCBD, that combines neuroimaging and behavioural phenotypes with perinatal factors at county/region/central district hospitals. PeriCBD was designed to establish a platform for the investigation of individual differences in brain-mind development associated with perinatal factors among children aged 3-10 years. Ultimately, our goal is to help understand how different adverse perinatal factors specifically impact cognitive development and neurodevelopment. Herein, we provide a systematic overview of the data acquisition/cleaning/quality control/sharing, processes of periCBD.

Role played by MDSC in colitis-associated colorectal cancer and potential therapeutic strategies.

Colitis-associated colorectal cancer has been a hot topic in public health issues worldwide. Numerous studies have demonstrated the significance of myeloid-derived suppressor cells (MDSCs) in the progression of this ailment, but the specific mechanism of their role in the transformation of inflammation to cancer is unclear, and potential therapies targeting MDSC are also unclear. This paper outlines the possible involvement of MDSC to the development of colitis-associated colorectal cancer. It also explores the immune and other relevant roles played by MDSC, and collates relevant targeted therapies against MDSC. In addition, current targeted therapies for colorectal cancer are analyzed and summarized.

Modulating the Coordination Chemistry of Co-N-C Catalytic Sites by Ru Species to Accelerate the Polysulfide Conversion Kinetics in Lithium-Sulfur Batteries.

The performance of lithium-sulfur batteries is compromised by the loss of sulfur as dissolved polysulfides in the electrolyte and consequently the polysulfide redox shutting effect. Accelerating the conversion kinetics of polysulfide intermediates into sulfur or lithium sulfide through electrocatalysis has emerged as a root-cause solution. Co-N-C composite electrocatalyst is commonly used for this purpose. It is illustrated here that how the effectiveness can be improved by modulating the coordination chemistry of Co-N-C catalytic sites through introducing Ru species (RuCo-NC). The well-dispersed Ru in the Co-NC carbon matrix altered the total charge distribution over the Co-N-C catalytic sites and led to the formation of electron-rich Co-N, which is highly active for the polysulfide conversion reactions. Using Ru to modulate the electronic structure in the Co-N-C configuration and the additional catalytic sites over the Ru-Nx species can manifest optimal adsorption behavior of polysulfides. Consequently, the sulfur cathode with RuCo-NC can reduce the capacity fade rate from 0.11% per cycle without catalyst (initial capacity of 701 mAh g-1) to 0.054% per cycle (initial capacity of 1074 mAh g-1) over 400 cycles at 0.2 C rate. The results of this study provide the evidence for a feasible catalyst modification strategy for the polysulfide electrocatalysis.

Elevated serum levels of soluble B-cell maturation antigen as a prognostic biomarker for multiple myeloma.

Serum B-cell maturation antigen (sBCMA) levels can serve as a sensitive biomarker in multiple myeloma (MM). In the research setting, sBCMA levels can be accurately detected by enzyme-linked immunosorbent assay (ELISA), but the approach has not been approved for clinical use. Here, we used a novel chemiluminescence method to assess sBCMA levels in 759 serum samples from 17 healthy donors and 443 patients with plasma cell (PC) diseases including AL amyloidosis, POEMS syndrome and MM. Serum BCMA levels were elevated 16.1-fold in patients with newly diagnosed MM compared to healthy donors and rare PC diseases patients. Specifically, the sBCMA levels in patients with progressive disease were 64.6-fold higher than those who showed partial response or above to treatment. The sBCMA level also correlated negatively with the response depth of MM patients. In newly diagnosed and relapsed MM patients, survival was significantly longer among those subjects whose sBCMA levels are below the median levels compared with those above the median value. We optimized the accuracy of the survival prediction further by integrating sBCMA level into the Second Revised International Staging System (R2-ISS). Our findings provide evidence that the novel chemiluminescence method is sensitive and practical for measuring sBCMA levels in clinical samples and confirm that sBCMA might serve as an independent prognostic biomarker for MM.

Reviving Static Charts into Live Charts.

Data charts are prevalent across various fields due to their efficacy in conveying complex data relationships. However, static charts may sometimes struggle to engage readers and efficiently present intricate information, potentially resulting in limited understanding. We introduce "Live Charts," a new format of presentation that decomposes complex information within a chart and explains the information pieces sequentially through rich animations and accompanying audio narration. We propose an automated approach to revive static charts into Live Charts. Our method integrates GNN-based techniques to analyze the chart components and extract data from charts. Then we adopt large natural language models to generate appropriate animated visuals along with a voice-over to produce Live Charts from static ones. We conducted a thorough evaluation of our approach, which involved the model performance, use cases, a crowd-sourced user study, and expert interviews. The results demonstrate Live Charts offer a multi-sensory experience where readers can follow the information and understand the data insights better. We analyze the benefits and drawbacks of Live Charts over static charts as a new information consumption experience.

Research progress on the role of macrophages in acne and regulation by natural plant products.

Acne vulgaris is one of the most common skin diseases. The current understanding of acne primarily revolves around inflammatory responses, sebum metabolism disorders, aberrant hormone and receptor expression, colonization by Cutibacterium acnes, and abnormal keratinization of follicular sebaceous glands. Although the precise mechanism of action remains incompletely understood, it is plausible that macrophages exert an influence on these pathological features. Macrophages, as a constituent of the human innate immune system, typically manifest distinct phenotypes across various diseases. It has been observed that the polarization of macrophages toward the M1 phenotype plays a pivotal role in the pathogenesis of acne. In recent years, extensive research on acne has revealed an increasing number of natural remedies exhibiting therapeutic efficacy through the modulation of macrophage polarization. This review investigates the role of cutaneous macrophages, elucidates their potential significance in the pathogenesis of acne, a prevalent chronic inflammatory skin disorder, and explores the therapeutic mechanisms of natural plant products targeting macrophages. Despite these insights, the precise role of macrophages in the pathogenesis of acne remains poorly elucidated. Subsequent investigations in this domain will further illuminate the pathogenesis of acne and potentially offer guidance for identifying novel therapeutic targets for this condition.

Aberrant cortical morphology patterns are associated with cognitive impairment in patients with chronic heart failure.

A mounting body of evidences suggests that patients with chronic heart failure (HF) frequently experience cognitive impairments, but the neuroanatomical mechanism underlying these impairments remains elusive. In this retrospective study, 49 chronic HF patients and 49 healthy controls (HCs) underwent brain structural MRI scans and cognitive assessments. Cortical morphology index (cortical thickness, complexity, sulcal depth and gyrification) were evaluated. Correlations between cortical morphology and cognitive scores and clinical variables were explored. Logistic regression analysis was employed to identify risk factors for predicting 3-year major adverse cardiovascular events. Compared with HCs, patients with chronic HF exhibited decreased cognitive scores (p < .001) and decreased cortical thickness, sulcal depth and gyrification in brain regions involved cognition, sensorimotor, autonomic nervous system (family-wise error correction, all p values <.05). Notably, HF duration and New York Heart Association (NYHA) demonstrated negative correlations with abnormal cortex morphology, particularly HF duration and thickness in left precentral gyrus (r = -.387, p = .006). Cortical morphology characteristics exhibited positive associations with global cognition, particularly cortical thickness in left pars opercularis (r = .476, p < .001). NYHA class is an independent risk factor for adverse outcome (p = .001). The observed correlation between abnormal cortical morphology and global cognition suggested that cortical morphology may serve as a promising imaging biomarker and provide insights into neuroanatomical underpinnings of cognitive impairment in patients with chronic HF.

Targeting gut microbiota for immunotherapy of diseases.

With advances in next-generation sequencing technology, there is growing evidence that the gut microbiome plays a key role in the host's innate and adaptive immune system. Gut microbes and their metabolites directly or indirectly regulate host immune cells. Crucially, dysregulation of the gut microbiota is often associated with many immune system diseases. In turn, microbes modulate disease immunotherapy. Data from preclinical to clinical studies suggest that the gut microbiota may influence the effectiveness of tumor immunotherapy, particularly immune checkpoint inhibitors (ICIs). In addition, the most critical issue now is a COVID-19 vaccine that generates strong and durable immunity. A growing number of clinical studies confirm the potential of gut microbes to enhance the efficacy of COVID-19 vaccines. However, it is still unclear how gut bacteria interact with immune cells and what treatments are based on gut microbes. Here, we outline recent advances in the effects and mechanisms of the gut microbiota and its metabolites (tryptophan metabolites, bile acids, short-chain fatty acids, and inosine) on different immune cells (dendritic cells, CD4+T cells, and macrophages). It also highlights innovative intervention strategies and clinical trials of microbiota-based checkpoint blocking therapies for tumor immunity, and ongoing efforts to maintain the long-term immunogenicity of COVID-19 vaccines. Finally, the challenges to be overcome in this area are discussed. These provide an important basis for further research and clinical translation of gut microbiota.

Natural products as the therapeutic strategies for addiction.

World Drug Report 2023 concluded that 296 million people abused drugs, 39.5 million became addiction and 494,000 died as a direct or indirect result of addiction. Addiction has become a growing problem that affects individuals, their families, societies, countries and even the world. However, treatment for addiction is only limited to some developed countries because of the high cost, difficult implementation, and time consuming. Therefore, there is an urgent need to develop a low-cost, effective drug for the development of addiction treatment in more countries, which is essential for the stability and sustainable development of the world. In this review, it provided an overview of the abuse of common addictive drugs, related disorders, and current therapeutic regimen worldwide, and summarized the mechanisms of drug addiction as reward circuits, neuroadaptation and plasticity, cognitive decision-making, genetics, and environment. According to their chemical structure, 43 natural products and 5 herbal combinations with potential to treat addiction were classified, and their sources, pharmacological effects and clinical trials were introduced. It was also found that mitragine, ibogine, L-tetrahydropalmatine and crocin had greater potential for anti-addiction.

Vacancy-Driven High-Performance Metabolic Assay for Diagnosis and Therapeutic Evaluation of Depression.

Depression is one of the most common mental illnesses and is a well-known risk factor for suicide, characterized by low overall efficacy (<50%) and high relapse rate (40%). A rapid and objective approach for screening and prognosis of depression is highly desirable but still awaits further development. Herein, a high-performance metabolite-based assay to aid the diagnosis and therapeutic evaluation of depression by developing a vacancy-engineered cobalt oxide (Vo-Co3O4) assisted laser desorption/ionization mass spectrometer platform is presented. The easy-prepared nanoparticles with optimal vacancy achieve a considerable signal enhancement, characterized by favorable charge transfer and increased photothermal conversion. The optimized Vo-Co3O4 allows for a direct and robust record of plasma metabolic fingerprints (PMFs). Through machine learning of PMFs, high-performance depression diagnosis is achieved, with the areas under the curve (AUC) of 0.941-0.980 and an accuracy of over 92%. Furthermore, a simplified diagnostic panel for depression is established, with a desirable AUC value of 0.933. Finally, proline levels are quantified in a follow-up cohort of depressive patients, highlighting the potential of metabolite quantification in the therapeutic evaluation of depression. This work promotes the progression of advanced matrixes and brings insights into the management of depression.

Altered effective connectivity among face-processing systems in major depressive disorder.

BACKGROUND: Neuroimaging studies have revealed abnormal functional interaction during the processing of emotional faces in patients with major depressive disorder (MDD), thereby enhancing our comprehension of the pathophysiology of MDD. However, it is unclear whether there is abnormal directional interaction among face-processing systems in patients with MDD. METHODS: A group of patients with MDD and a healthy control group underwent a face-matching task during functional magnetic resonance imaging. Dynamic causal modelling (DCM) analysis was used to investigate effective connectivity between 7 regions in the face-processing systems. We used a Parametric Empirical Bayes model to compare effective connectivity between patients with MDD and controls. RESULTS: We included 48 patients and 44 healthy controls in our analyses. Both groups showed higher accuracy and faster reaction time in the shape-matching condition than in the face-matching condition. However, no significant behavioural or brain activation differences were found between the groups. Using DCM, we found that, compared with controls, patients with MDD showed decreased self-connection in the right dorsolateral prefrontal cortex (DLPFC), amygdala, and fusiform face area (FFA) across task conditions; increased intrinsic connectivity from the right amygdala to the bilateral DLPFC, right FFA, and left amygdala, suggesting an increased intrinsic connectivity centred in the amygdala in the right side of the face-processing systems; both increased and decreased positive intrinsic connectivity in the left side of the face-processing systems; and comparable task modulation effect on connectivity. LIMITATIONS: Our study did not include longitudinal neuroimaging data, and there was limited region of interest selection in the DCM analysis. CONCLUSION: Our findings provide evidence for a complex pattern of alterations in the face-processing systems in patients with MDD, potentially involving the right amygdala to a greater extent. The results confirm some previous findings and highlight the crucial role of the regions on both sides of face-processing systems in the pathophysiology of MDD.

Transcription factors Pbr3RAV2 and PbrTTG1 regulate pear resistance to Botryosphaeria dothidea via the autophagy pathway.

Pear ring rot, caused by Botryosphaeria dothidea, is the most serious disease of pear (Pyrus spp.) trees. However, the molecular mechanisms underlying pear resistance to B. dothidea remain elusive. Herein, we demonstrated that the pear AuTophagy-related Gene 1a (PbrATG1a) plays a key role in autophagic activity and resistance to B. dothidea. Stable overexpression of PbrATG1a enhanced resistance to B. dothidea in pear calli. Autophagy activity was greater in PbrATG1a overexpressing calli than in WT calli. We used yeast one-hybrid screening to identify a transcription factor, Related to ABI3 and VP1 (Pbr3RAV2), that binds the promoter of PbrATG1a and enhances pear resistance to B. dothidea by regulating autophagic activity. Specifically, overexpression of Pbr3RAV2 enhanced resistance to B. dothidea in pear calli, while transient silencing of Pbr3RAV2 resulted in compromised resistance to B. dothidea in Pyrus betulaefolia. In addition, we identified Transparent Testa Glabra 1 (PbrTTG1), which interacts with Pbr3RAV2. Pathogen infection enhanced the interaction between Pbr3RAV2 and PbrTTG1. The Pbr3RAV2-PbrTTG1 complex increased the binding capacity of Pbr3RAV2 and transcription of PbrATG1a. In addition to providing insights into the molecular mechanisms underlying pear disease resistance, these findings suggest potential genetic targets for enhancing disease resistance in pear.

Interrelationships among abnormal P-wave axis, metabolic syndrome and its components, and mortality in US adults.

BACKGROUND: Metabolic syndrome (MetS) is associated with increased rates of cardiovascular disease and mortality and is linked to abnormal electrocardiogram (ECG) parameters. We aimed to explore the relationships and interactions among MetS and its components, abnormal P-wave axis (aPWA), and mortality rates. METHODS: We analyzed data from 7526 adult participants with sinus rhythm recruited from the National Health and Nutrition Examination Survey III. MetS was classified based on the NCEP ATP III-2005 definition. aPWA included all P-wave axis outside 0-75°. The National Death Index was utilized to identify survival status. Hazard ratios (HRs) and 95% confidence intervals (CIs) categorized by aPWA, MetS, and their components were analyzed using Cox proportional hazards models to investigate all-cause and cardiovascular mortalities. RESULTS: Within a median follow-up period of 20.76 years, 4686 deaths were recorded, of which 1414 were attributable to cardiovascular disease. Participants with both MetS and aPWA had higher all-cause (HR: 1.45, 95% CI: 1.29-1.64, interaction P = 0.043) and cardiovascular (HR: 1.36, 95% CI: 1.02-1.79, interaction P-value = 0.058) mortality rates than participants without MetS and with a normal P-wave axis. Participants with the greatest number of MetS components and aPWA had a higher risk of all-cause mortality (HR: 1.70, 95% CI: 1.13-2.55, P = 0.011). CONCLUSIONS: Individuals with both aPWA and MetS have a higher risk of mortality, and those with a greater number of MetS components and aPWA have a higher risk of all-cause mortality. These findings highlight the significance of integrating ECG characteristics with metabolic health status in clinical assessment.

Subregion-specific 18F-FDG PET-CT radiomics for the pre-treatment prediction of EGFR mutation status in solid lung adenocarcinoma.

This study aimed to assess the efficacy of fluor-18 fluorodeoxyglucose (18F-FDG) PET/CT using sub-regional-based radiomics in predicting epidermal growth factor receptor (EGFR) mutation status in pretreatment patients with solid lung adenocarcinoma. A retrospective analysis included 269 patients (134 EGFR+ and 135 EGFR-) who underwent pretreatment 18F-FDG PET/CT scans and EGFR mutation testing. The most metabolically active intratumoral sub-region was identified, and radiomics features from whole tumors or sub-regional regions were used to build classification models. The dataset was split into a 7:3 ratio for training and independent testing. Feature subsets were determined by Pearson correlation and the Kruskal Wallis test and radiomics classifiers were built with support vector machines or logistic regressions. Evaluation metrics, including accuracy, area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were employed for different classifiers. Results indicated that the sub-region-based classifier outperformed the whole-tumor classifier in terms of accuracy (73.8% vs. 66.2%), AUC (0.768 vs. 0.632), specificity (65.0% vs. 50.0%), PPV (70.2% vs. 62.2%), and NPV (78.8% vs. 74.0%). The clinical classifier exhibited an accuracy of 75.0%, AUC of 0.768, sensitivity of 72.5%, specificity of 77.5%, PPV of 76.3%, and NPV of 73.8%. The combined classifier, incorporating sub-region analysis and clinical parameters, demonstrated further improvement with an accuracy of 77.5%, AUC of 0.807, sensitivity of 77.5%, specificity of 77.5%, and NPV of 77.5%. The study suggests that sub-region-based 18F-FDG PET/CT radiomics enhances EGFR mutation prediction in solid lung adenocarcinoma, providing a practical and cost-efficient alternative to invasive EGFR testing.

Clinical evidence for the prognostic impact of metformin in cancer patients treated with immune checkpoint inhibitors.

BACKGROUND: Preclinical studies suggest that metformin might enhance the efficacy of immune checkpoint inhibitors (ICIs) and potentially influence the prognoses of cancer patients undergoing ICIs treatment. This study endeavors to assess the prognostic significance of metformin in cancer patients undergoing ICIs therapy, aiming to furnish evidence-based insights for clinical practice. METHODS: A thorough literature search was conducted across electronic databases to encompass all potential records published before November 20th, 2023. A meta-analysis was executed utilizing Stata 17.0 to derive pooled hazard ratios (HRs) with 95% confidence intervals (CIs) for both overall survival (OS) and progression-free survival (PFS). RESULTS: A total of 22 studies encompassing 9,011 patients met the inclusion criteria. Meta-analyses revealed a significant correlation between metformin use and poorer OS (HR, 1.13; 95 %CI, 1.04-1.23; P = 0.004) rather than PFS (HR, 1.04; 95 %CI, 0.96-1.14; P = 0.345) among cancer patients undergoing ICIs treatment. Subgroup analysis delineated that the concurrent administration of metformin and ICIs significantly associated with adverse prognoses in the European population (OS: HR, 1.23; 95 %CI, 1.10-1.39; P = 0.001; PFS: HR, 1.14; 95 %CI, 1.02-1.28; P = 0.024). CONCLUSION: Based on current clinical evidence, concomitant metformin use does not appear to improve the prognostic outcomes for cancer patients undergoing ICIs therapy and may potentially correlate with inferior prognoses. Further studies are imperative to comprehensively elucidate the impact of metformin within the realm of ICIs therapy.

Exploring causality in the association between gut microbiota and irritable bowel syndrome risk: a large Mendelian randomization study.

BACKGROUND: In the past, some observational studies have highlighted the correlation between gut microbiota and irritable bowel syndrome (IBS). However, it is still unknown if the composition of gut microbiota shows a causal effect on the risk of IBS. AIM: To conduct Mendelian randomization (MR) analysis of the samples to study the probable causal relationship between the gut microbiota, their taxonomic groups, and the risk of IBS. MATERIALS AND METHODS: In this study, the summarized data regarding 211 gut microbiota and their IBS genome-wide association studies (GWAS) were collected from public databases. The causal estimates were determined using five MR techniques, where Inverse Variance Weighted (IVW) regression was employed as the major MR technique. Herein, MR-PRESSO and MR-Egger intercept tests were conducted to prevent horizontal pleiotropy. Cochran's Q test was used to evaluate heterogeneity using the IVW and MR-Egger techniques. RESULTS: IVW results showed that gut microbes, belonging to Class Gammaproteobacteria (P = 0.04; OR = 1.45), Family XIII (P = 0.03; OR = 1.34), Family Prevotellaceae (P = 0.003; OR =1.24), and Lachnospiraceae UCG004 (P = 0.049; OR = 1.19) increased the risk of IBS, while Alcaligenaceae (P = 0.03; OR = 0.83, 95% CI: 0.69-0.98) and Coprobacter (P = 0.02; OR = 0.86, 95% CI: 0.76-0.98) decreased the risk of IBS. CONCLUSIONS: This study presented novel insights that highlighted the causal relationship between gut microbiota and IBS, and offered new treatment strategies for preventing or treating IBS.

Comprehensive evaluation of immune dysregulation in secondary hemophagocytic lymphohistiocytosis.

Host immune dysfunction plays a crucial role in the onset, progression, and outcome of hemophagocytic lymphohistiocytosis (HLH). This study aimed to comprehensively evaluate the peripheral immune profiles in patients with newly diagnosed secondary hemophagocytic lymphohistiocytosis (sHLH), and explore their predictive value for patient prognosis. A total of 77 patients with sHLH were enrolled in this study, with 31 of them experiencing mortality. Flow cytometry was used to assess the percentages, absolute numbers, and phenotypes of lymphocyte subsets. Simultaneously, cytokine levels and routine laboratory indicators were also collected. In sHLH patients, lymphocyte subset absolute numbers were significantly impaired, accompanied by T cell hyperactivation, B cell hyperactivation, and increased plasmablast proliferation. Prognostic analysis revealed that lower CD8+ T cell percentages, elevated APTT, IL-6, IL-10 levels, and increased CD4+CD28null T cell proportions were associated with poor patient outcomes. The study demonstrates dysregulation in the counts and phenotypes of lymphocyte subsets in sHLH patients. Several key factors, including IL-6, IL-10, APTT, and various T cell percentages, have potential as prognostic markers and therapeutic targets in sHLH.

Understanding the charge transfer effects of single atoms for boosting the performance of Na-S batteries.

The effective flow of electrons through bulk electrodes is crucial for achieving high-performance batteries, although the poor conductivity of homocyclic sulfur molecules results in high barriers against the passage of electrons through electrode structures. This phenomenon causes incomplete reactions and the formation of metastable products. To enhance the performance of the electrode, it is important to place substitutable electrification units to accelerate the cleavage of sulfur molecules and increase the selectivity of stable products during charging and discharging. Herein, we develop a single-atom-charging strategy to address the electron transport issues in bulk sulfur electrodes. The establishment of the synergistic interaction between the adsorption model and electronic transfer helps us achieve a high level of selectivity towards the desirable short-chain sodium polysulfides during the practical battery test. These finding indicates that the atomic manganese sites have an enhanced ability to capture and donate electrons. Additionally, the charge transfer process facilitates the rearrangement of sodium ions, thereby accelerating the kinetics of the sodium ions through the electrostatic force. These combined effects improve pathway selectivity and conversion to stable products during the redox process, leading to superior electrochemical performance for room temperature sodium-sulfur batteries.

Effects of a color gradient and emoji in AR-HUD warning interfaces in autonomous vehicles on takeover performance and driver emotions.

OBJECTIVE: This study examined the effects of color gradients and emojis in an augmented reality-head-up display (AR-HUD) warning interface on driver emotions and takeover performance. METHODS: A total of 48 participants were grouped into four different warning interfaces for a simulated self-driving takeover experiment. Two-way analysis of variance and the Kruskal-Wallis test was used to analyze takeover time, mood, task load, and system availability. RESULTS: Takeover efficiency and task load did not significantly differ among the interfaces, but the interfaces with a color gradient and emoji positively affected drivers' emotions. Emojis also positively affected emotional valence, and the color gradient had a high emotional arousal effect. Both the color gradient and the emoji interfaces had an inhibitory effect on negative emotions. The emoji interface was easier to learn, reducing driver learning costs. CONCLUSIONS: These findings offer valuable insights for designing safer and more user-friendly AR-HUD interfaces for self-driving cars.

Analysis of the Immune Response by Standardized Whole-Blood Stimulation with Metabolism Modulation.

The immune system defends the body from infection and plays a vital role in a wide range of health conditions. Metabolism affects a series of physiological processes, including those linked to the function of human immune system. Cellular metabolism modulates immune cell activation and cytokine production. Understanding the relationship between metabolism and immune response has important implications for the development of immune-based therapeutics. However, the deployment of large-scale functional assays to investigate the metabolic regulation of immune response has been limited by the lack of standardized procedures. Here, we present a protocol for the analysis of immune response using standardized whole-blood stimulation with metabolism modulation. Diverse immune stimuli including pattern recognition receptor (PRR) ligands and microbial stimuli were incubated with fresh human whole blood. The metabolic inhibitors were used to modulate metabolic status in the immune cells. The variable immune responses after metabolic interventions were evaluated. We described in detail the main steps involved in the whole-blood stimulation and cytokines quantification, namely, collection and treatment of whole blood, preparation of samples and controls, cytokines detection, and stimulation with metabolic interventions. The metabolic inhibitors for anabolic pathways and catabolic pathways exert selective effects on the production of cytokines from immune cells. In addition to a robust and accurate assessment of immune response in cohort studies, the standardized whole-blood stimulation with metabolic regulation might provide new insights for modulating immunity. Supplementary Information: The online version contains supplementary material available at 10.1007/s43657-023-00114-0.