Quantitative Electroencephalography in Term Neonates During the Early Postnatal Period Across Various Sleep States.

Background: Neonatal sleep is pivotal for their growth and development, yet manual interpretation of raw images is time-consuming and labor-intensive. Quantitative Electroencephalography (QEEG) presents significant advantages in terms of objectivity and convenience for investigating neonatal sleep patterns. However, research on the sleep patterns of healthy neonates remains scarce. This study aims to identify QEEG markers that distinguish between different neonatal sleep cycles and analyze QEEG alterations across various sleep stages in relation to postmenstrual age. Methods: From September 2023 to February 2024, full-term neonates admitted to the neonatology department at the Obstetrics and Gynecology Hospital of Fudan University were enrolled in this study. Electroencephalographic (EEG) recordings were obtained from neonates aged 37-42 weeks, within 1-7 days post-birth. The ROC curve was employed to evaluate QEEG features related to amplitude, range EEG (rEEG), spectral density, and connectivity across different sleep stages. Furthermore, regression analyses were performed to investigate the association between these QEEG characteristics and postmenstrual age. Results: The alpha frequency band's spectral_diff_F3 emerged as the most potent discriminator between active sleep (AS) and quiet sleep (QS). In distinguishing AS from wakefulness (W), the theta frequency's spectral_diff_C4 was the most effective, whereas the delta frequency's spectral_diff_P4 excelled in differentiating QS from W. During AS and QS phases, there was a notable increase in entropy within the delta frequency band across all monitored brain regions and in the spectral relative power within the theta frequency band, correlating with postmenstrual age (PMA). Conclusion: Spectral difference showcases the highest discriminative capability across awake and various sleep states. The observed patterns of neonatal QEEG alterations in relation to PMA are consistent with the maturation of neonatal sleep, offering insights into the prediction and evaluation of brain development outcomes.

LANMAO sleep recorder versus polysomnography in neonatal EEG recording and sleep analysis.

BACKGROUND: The field of neonatal sleep analysis is burgeoning with devices that purport to offer alternatives to polysomnography (PSG) for monitoring sleep patterns. However, the majority of these devices are limited in their capacity, typically only distinguishing between sleep and wakefulness. This study aims to assess the efficacy of a novel wearable electroencephalographic (EEG) device, the LANMAO Sleep Recorder, in capturing EEG data and analyzing sleep stages, and to compare its performance against the established PSG standard. METHODS: The study involved concurrent sleep monitoring of 34 neonates using both PSG and the LANMAO device. Initially, the study verified the consistency of raw EEG signals captured by the LANMAO device, employing relative spectral power analysis and Pearson correlation coefficients (PCC) for validation. Subsequently, the LANMAO device's integrated automated sleep staging algorithm was evaluated by comparing its output with expert-generated sleep stage classifications. RESULTS: Analysis revealed that the PCC between the relative spectral powers of various frequency bands during different sleep stages ranged from 0.28 to 0.48. Specifically, the correlation for delta waves was recorded at 0.28. The automated sleep staging algorithm of the LANMAO device demonstrated an overall accuracy of 79.60 %, Cohen kappa of 0.65, and F1 Score of 76.93 %. Individual accuracy for Wake at 87.20 %, NREM at 85.70 %, and REM Sleep at 81.30 %. CONCLUSION: While the LANMAO Sleep Recorder's automated sleep staging algorithm necessitates further refinement, the device shows promise in accurately recording neonatal EEG during sleep. Its potential for minimal invasiveness makes it an appealing option for monitoring sleep conditions in newborns, suggesting a novel approach in the field of neonatal sleep analysis.

Neuroprotective effects of chlorogenic acid: Modulation of Akt/Erk1/2 signaling to prevent neuronal apoptosis in Parkinson's disease.

As a prevalent neurodegenerative disorder, Parkinson's disease is associated with oxidative stress. Our recent investigations revealed that reactive oxygen species (ROS) and PD-toxins like 6-hydroxydopamine (6-OHDA) can induce neuronal apoptosis through over-activation of Akt signaling. Chlorogenic acid (CGA), a natural acid phenol abundant in the human diet, is well-documented for its ability to mitigate intracellular ROS. In this study, we utilized CGA to treat experimental models of PD both in vitro and in vivo. Our study results demonstrated that SH-SY5Y and primary neurons exhibited cell apoptosis in response to 6-OHDA. Pretreatment with CGA significantly attenuated PD toxins-induced large amount of ROS, inhibiting Erk1/2 activation, preventing Akt inhibition, and hindering neuronal cell death. Combining the Erk1/2 inhibitor U0126 with CGA could reverse 6-OHDA-induced Akt inhibition, ROS, and apoptosis in the cells. Crucially, the Akt activator SC79 and ROS scavenger NAC both could eliminate excessive ROS via Akt and Erk1/2 signaling pathways, and CGA further potentiated these effects in PD models. Behavioral experiments revealed that CGA could alleviate gait abnormalities in PD model mice. The neuroprotective effects have been demonstrated in several endocrine regions and in the substantia nigra tissue, which shows the positive tyrosine hydroxylase (TH). Overall, our results suggest that CGA prevents the activation of Erk1/2 and inactivation of Akt by removing excess ROS in PD models. These findings propose a potential strategy for mitigating neuronal degeneration in Parkinson's disease by modulating the Akt/Erk1/2 signaling pathway through the administration of CGA and/or the use of antioxidants to alleviate oxidative stress.

Mechanistic insights into δ-MnO2/biochar-activated persulfate-treated wastewater containing antibiotics and heavy metals: Nonradical pathway and pivotal role of Cu(⠡) at low temperature.

Herein, we present a high efficiency system based on biochar loaded with layered manganese dioxide to remove tetracycline and heavy metals from livestock wastewater. Under the optimal conditions, the degradation efficiencies of TC in the δ-MnO2/BC/PS system were 85.5% at 25 °C and 38.5% at 5 °C. Radical quenching experiments revealed that radical reactions in the δ-MnO2/BC/PS system were weak under 15 °C. Adsorption degradation experiments showed that the system maintained good adsorption performance at 5 °C. Galvanic cell experiments and cyclic voltammetry showed that the δ-MnO2/BC material had good electrochemical activity and high stability in response to temperature, indicating that TC was degraded by a nonradical pathway that was not limited by temperature, such as electron transfer. Copper ion was important coadsorbent and coactivator of the reaction system. Furthermore, FTIR, XPS, and X-ray diffraction (XRD) analyses showed that Cu(II) in the system was involved in changing the manganese valence state in the δ-MnO2/BC material and increasing the -OH content of BC. Comparison of the different products generated during metabolic testing revealed that the reaction pathway of the system at low temperature (5 °C) differed from that at normal temperature (25 °C). The δ-MnO2/BC material demonstrated good removal ability for antibiotics and heavy metals at normal and low temperatures in actual biogas slurry. The study provides insight for improving the efficiency of environmentally friendly treatments of aquaculture wastewater in cold regions, which is of great significance for resource utilization.

Causal association between plasma metabolites and neurodegenerative diseases.

BACKGROUND: Establishing causal relationships between metabolic biomarkers and neurodegenerative diseases such as Alzheimer's disease (AD) and Parkinson's disease (PD) is a challenge faced by observational studies. In this study, our aim was to investigate the causal associations between plasma metabolites and neurodegenerative diseases using Mendelian Randomization (MR) methods. METHODS: We utilized genetic associations with 1400 plasma metabolic traits as exposures. We used large-scale genome-wide association study (GWAS) summary statistics for AD and PD as our discovery datasets. For validation, we performed repeated analyses using different GWAS datasets. The main statistical method employed was inverse variance-weighted (IVW). We also conducted enrichment pathway analysis for IVW-identified metabolites. RESULTS: In the discovered dataset, there are a total of 69 metabolites (36 negatively, 33 positively) potentially associated with AD, and 47 metabolites (24 negatively， 23 positively) potentially associated with PD. Among these, 4 significant metabolites overlap with significant metabolites (PIVW < 0.05)in the validation dataset for AD, and 1 metabolite overlaps with significant metabolites in the validation dataset for PD. Three metabolites serve as common potential metabolic markers for both AD and PD, including Tryptophan betaine, Palmitoleoylcarnitine (C16:1), and X-23655 levels. Further pathway enrichment analysis suggests that the SLC-mediated transmembrane transport pathway, involving tryptophan betaine and carnitine metabolites, may represent potential intervention targets for treating AD and PD. CONCLUSION: This study offers novel insights into the causal effects of plasma metabolites on degenerative diseases through the integration of genomics and metabolomics. The identification of metabolites and metabolic pathways linked to AD and PD enhances our comprehension of the underlying biological mechanisms and presents promising targets for future therapeutic interventions in AD and PD.

A Brain-Controlled and User-Centered Intelligent Wheelchair: A Feasibility Study.

Recently, due to physical aging, diseases, accidents, and other factors, the population with lower limb disabilities has been increasing, and there is consequently a growing demand for wheelchair products. Modern product design tends to be more intelligent and multi-functional than in the past, with the popularization of intelligent concepts. This supports the design of a new, fully functional, intelligent wheelchair that can assist people with lower limb disabilities in their day-to-day life. Based on the UCD (user-centered design) concept, this study focused on the needs of people with lower limb disabilities. Accordingly, the demand for different functions of intelligent wheelchair products was studied through a questionnaire survey, interview survey, literature review, expert consultation, etc., and the function and appearance of the intelligent wheelchair were then defined. A brain-machine interface system was developed for controlling the motion of the intelligent wheelchair, catering to the needs of disabled individuals. Furthermore, ergonomics theory was used as a guide to determine the size of the intelligent wheelchair seat, and eventually, a new intelligent wheelchair with the features of climbing stairs, posture adjustment, seat elevation, easy interaction, etc., was developed. This paper provides a reference for the design upgrade of the subsequently developed intelligent wheelchair products.

Methods and clinical biomarker discovery for targeted proteomics using Olink technology.

PURPOSE: This paper is to offer insights for designing research utilizing Olink technology to identify biomarkers and potential therapeutic targets for disease treatment. EXPERIMENTAL DESIGN: We discusses the application of Olink technology in oncology, cardiovascular, respiratory and immune-related diseases, and Outlines the advantages and limitations of Olink technology. RESULTS: Olink technology simplifies the search for therapeutic targets, advances proteomics research, reveals the pathogenesis of diseases, and ultimately helps patients develop precision treatments. CONCLUSIONS: Although proteomics technology has been rapidly developed in recent years, each method has its own disadvantages, so in the future research, more methods should be selected for combined application to verify each other.

Effect of sunlight on vitamin D and hemoglobin levels among the residents of Ningbo, China.

PURPOSE: this study investigated the effect of sunlight on vitamin D and hemoglobin levels among the residents of Ningbo, China. The impact of gender, age, and season on vitamin D and hemoglobin levels was also explored. METHODS: a total of 8,481 research subjects, including 5,146 men and 3,335 women, who were permanent residents of Ningbo and received health checkups at Ningbo Second Hospital, were included in the study. Ningbo City climate bulletin data from 2019 to 2022 was also included. RESULTS: the study subjects received an average of 132.20 ± 40.05 h of sunlight exposure per month and had average vitamin D levels of 19.63 ± 6.61 ng/ml. Hemoglobin levels were adequate in 85.4 % of the participants and deficient in 14.6 %. Sunlight exposure correlated positively with vitamin D and negatively with hemoglobin levels. Regression analysis indicated that gender, age, and season affected vitamin D and hemoglobin levels to different degrees. CONCLUSION: in Ningbo, vitamin D deficiency was common in adults while hemoglobin levels were mostly normal. The amount of sunlight exposure had a significant effect on vitamin D and hemoglobin levels and this relationship was impacted by gender, age, and season.

The molecular subtypes of autoimmune diseases.

Autoimmune diseases (ADs) are characterized by their complexity and a wide range of clinical differences. Despite patients presenting with similar symptoms and disease patterns, their reactions to treatments may vary. The current approach of personalized medicine, which relies on molecular data, is seen as an effective method to address the variability in these diseases. This review examined the pathologic classification of ADs, such as multiple sclerosis and lupus nephritis, over time. Acknowledging the limitations inherent in pathologic classification, the focus shifted to molecular classification to achieve a deeper insight into disease heterogeneity. The study outlined the established methods and findings from the molecular classification of ADs, categorizing systemic lupus erythematosus (SLE) into four subtypes, inflammatory bowel disease (IBD) into two, rheumatoid arthritis (RA) into three, and multiple sclerosis (MS) into a single subtype. It was observed that the high inflammation subtype of IBD, the RA inflammation subtype, and the MS "inflammation & EGF" subtype share similarities. These subtypes all display a consistent pattern of inflammation that is primarily driven by the activation of the JAK-STAT pathway, with the effective drugs being those that target this signaling pathway. Additionally, by identifying markers that are uniquely associated with the various subtypes within the same disease, the study was able to describe the differences between subtypes in detail. The findings are expected to contribute to the development of personalized treatment plans for patients and establish a strong basis for tailored approaches to treating autoimmune diseases.

Immune control in Kawasaki disease knowledge mapping: a bibliometric analysis.

BACKGROUND: Kawasaki disease is a systemic vascular disease with an unclear pathophysiology that primarily affects children under the age of five. Research on immune control in Kawasaki disease has been gaining attention. This study aims to apply a bibliometric analysis to examine the present and future directions of immune control in Kawasaki disease. METHODS: By utilizing the themes "Kawasaki disease," "Kawasaki syndrome," and "immune control," the Web of Science Core Collection database was searched for publications on immune control in Kawasaki disease. This bibliometric analysis was carried out using VOSviewers, CiteSpace, and the R package "bibliometrix." RESULTS: In total, 294 studies on immune control in Kawasaki disease were published in Web of Science Core Collection. The three most significant institutions were Chang Gung University, the University of California San Diego, and Kaohsiung Chang Gung Memorial Hospital. China, the United States, and Japan were the three most important countries. In this research field, Clinical and Experimental Immunology was the top-referred journal, while the New England Journal of Medicine was the most co-cited journal. The Web of Science Core Collection document by McCrindle BW et al. published in 2017 was the most cited reference. Additionally, the author keywords concentrated on "COVID-19," "SARS-CoV-2," and "multisystem inflammatory syndrome in children" in recent years. CONCLUSION: The research trends and advancements in immune control in Kawasaki disease are thoroughly summarised in this bibliometric analysis, which is the first to do so. The data indicate recent research frontiers and hot directions, making it easier for researchers to study the immune control of Kawasaki disease.

Discovery of Small Molecule Interleukin 17A Inhibitors with Novel Binding Mode and Stoichiometry: Optimization of DNA-Encoded Chemical Library Hits to In Vivo Active Compounds.

Dysregulation of IL17A drives numerous inflammatory and autoimmune disorders with inhibition of IL17A using antibodies proven as an effective treatment. Oral anti-IL17 therapies are an attractive alternative option, and several preclinical small molecule IL17 inhibitors have previously been described. Herein, we report the discovery of a novel class of small molecule IL17A inhibitors, identified via a DNA-encoded chemical library screen, and their subsequent optimization to provide in vivo efficacious inhibitors. These new protein-protein interaction (PPI) inhibitors bind in a previously undescribed mode in the IL17A protein with two copies binding symmetrically to the central cavities of the IL17A homodimer.

Organophosphate esters (OPEs) in corals of the South China Sea: Occurrence, distribution, and bioaccumulation.

Organophosphate esters (OPEs) have garnered significant attention in recent years. In view of the enormous ecosystem services value and severe degradation of coral reefs in the South China Sea, this study investigated the occurrence, distribution, and bioaccumulation of 11 OPEs in five coral regions: Daya Bay (DY), Weizhou Island (WZ), Sanya Luhuitou (LHT), Xisha (XS) Islands, and Nansha (NS) Islands. Although OPEs were detected at a high rate, their concentration in South China Sea seawater (1.56 ± 0.89 ng L-1) remained relatively low compared to global levels. All OPEs were identified in coral tissues, with Luhuitou (575 ± 242 ng g-1 dw) showing the highest pollution levels, attributed to intense human activities. Coral mucus, acting as a defense against environmental stresses, accumulated higher ∑11OPEs (414 ± 461 ng g-1 dw) than coral tissues (412 ± 197 ng g-1 dw) (nonparametric test, p < 0.05), and their compositional characteristics varied greatly. In the case of harsh aquatic environments, corals increase mucus secretion and then accumulate organic pollutants. Tissue-mucus partitioning varied among coral species. Most OPEs were found to be bioaccumulative (BAFs >5000 L kg-1) in a few coral tissue samples besides Triphenyl phosphate (TPHP). Mucus' role in the bioaccumulation of OPEs in coral shouldn't be ignored.

Knowledge mapping of neonatal electroencephalogram: A bibliometric analysis (2004-2022).

BACKGROUND: Electroencephalography (EEG), a widely used noninvasive neurophysiological diagnostic tool, has experienced substantial advancements from 2004 to 2022, particularly in neonatal applications. Utilizing a bibliometric methodology, this study delineates the knowledge structure and identifies emergent trends within neonatal EEG research. METHODS: An exhaustive literature search was conducted on the Web of Science Core Collection (WoSCC) database to identify publications related to neonatal EEG from 2004 to 2022. Analytical tools such as VOSviewer, CiteSpace, and the R package "bibliometrix" were employed to facilitate this investigation. RESULTS: The search yielded 2501 articles originating from 79 countries, with the United States and England being the predominant contributors. A yearly upward trend in publications concerning neonatal EEG was observed. Notable research institutions leading this field include the University of Helsinki, University College London, and University College Cork. Clinical Neurophysiology is identified as the foremost journal in this realm, with Pediatrics as the most frequently co-cited journal. The collective body of work from 9977 authors highlights Sampsa Vanhatalo as the most prolific contributor, while Mark Steven Scher is recognized as the most frequently co-cited author. Key terms such as "seizures," "epilepsy," "hypoxic-ischemic encephalopathy," "amplitude-integrated EEG," and "brain injury" represent the focal research themes. CONCLUSION: This bibliometric analysis offers the first comprehensive review, encapsulating research trends and progress in neonatal EEG. It reveals current research frontiers and crucial directions, providing an essential resource for researchers engaged in neonatal neuroscience.

Occurrence, distribution, and sources of organophosphate esters (OPEs) in the air of the Indo-China Peninsula Based on a Passive Air Monitoring Network.

Organophosphate esters (OPEs) are a class of emerging and ubiquitous contaminants that are attracting increasing attention, and their large-scale use as flame retardants and plasticizers has led to their pervasive presence in the environment, although their broader impacts remain unknown. In this study, 11 OPEs were measured in the atmosphere of Southeast Asia and Southwest China during 2016. The ∑11OPEs were higher in this region (78.0-1670 pg/m3, mean 458 pg/m3) than in many remote areas, lower than in developed regions, and comparable to levels in many developing country cities. Generally, the ∑11OPEs were higher in urban (105-1670 pg/m3, mean 538 pg/m3) than in suburban (78.0-1350 pg/m3, mean 388 pg/m3). Seasonal variations of OPEs in the air were more pronounced in Cambodia and Laos, especially for Triphenyl Phosphate (TPHP). Seasonal variations of ∑11OPEs in most regions correspond to changes in temperature and rainfall. Biomass burning may be also a factor in facilitating OPE emissions from biomass materials or soil into the atmosphere of Southeast Asia. The random forest analysis showed that among these, rainfall had the greatest effect on the seasonal variation of atmospheric OPE concentrations, followed by biomass burning and temperature. The inter-regional variation of ∑11OPEs in Southeast Asia was related to population and economic development in each region. Airflow trajectories indicated that the OPEs in this region were mainly from local sources. The health risk assessment revealed that the inhalation exposure risks of OPEs to the residents in the study areas were very low during the sampling period, but may be increasing.

A machine learning algorithm based on circulating metabolic biomarkers offers improved predictions of neurological diseases.

BACKGROUND AND AIMS: A machine learning algorithm based on circulating metabolic biomarkers for the predictions of neurological diseases (NLDs) is lacking. To develop a machine learning algorithm to compare the performance of a metabolic biomarker-based model with that of a clinical model based on conventional risk factors for predicting three NLDs: dementia, Parkinson's disease (PD), and Alzheimer's disease (AD). MATERIALS AND METHODS: The eXtreme Gradient Boosting (XGBoost) algorithm was used to construct a metabolic biomarker-based model (metabolic model), a clinical risk factor-based model (clinical model), and a combined model for the prediction of the three NLDs. Risk discrimination (c-statistic), net reclassification improvement (NRI) index, and integrated discrimination improvement (IDI) index values were determined for each model. RESULTS: The results indicate that incorporation of metabolic biomarkers into the clinical model afforded a model with improved performance in the prediction of dementia, AD, and PD, as demonstrated by NRI values of 0.159 (0.039-0.279), 0.113 (0.005-0.176), and 0.201 (-0.021-0.423), respectively; and IDI values of 0.098 (0.073-0.122), 0.070 (0.049-0.090), and 0.085 (0.068-0.101), respectively. CONCLUSION: The performance of the model based on circulating NMR spectroscopy-detected metabolic biomarkers was better than that of the clinical model in the prediction of dementia, AD, and PD.

Increase in Agricultural-Derived NHx and Decrease in Coal Combustion-Derived NOx Result in Atmospheric Particulate N-NH4+ Surpassing N-NO3- in the South China Sea.

The atmospheric deposition of anthropogenic active nitrogen significantly influences marine primary productivity and contributes to eutrophication. The form of nitrogen deposition has been evolving annually, alongside changes in human activities. A disparity arises between observation results and simulation conclusions due to the limited field observation and research in the ocean. To address this gap, our study undertook three field cruises in the South China Sea in 2021, the largest marginal sea of China. The objective was to investigate the latest atmospheric particulate inorganic nitrogen deposition pattern and changes in nitrogen sources, employing nitrogen-stable isotopes of nitrate (δ15N-NO3-) and ammonia (δ15N-NH4+) linked to a mixing model. The findings reveal that the N-NH4+ deposition generally surpasses N-NO3- deposition, attributed to a decline in the level of NOx emission from coal combustion and an upswing in the level of NHx emission from agricultural sources. The disparity in deposition between N-NH4+ and N-NO3- intensifies from the coast to the offshore, establishing N-NH4+ as the primary contributor to oceanic nitrogen deposition, particularly in ocean background regions. Fertilizer (33 ± 21%) and livestock (20 ± 6%) emerge as the primary sources of N-NH4+. While coal combustion continues to be a significant contributor to marine atmospheric N-NO3-, its proportion has diminished to 22 (Northern Coast)-35% (background area) due to effective NOx emission controls by the countries surrounding the South China Sea, especially the Chinese Government. As coal combustion's contribution dwindles, the significance of vessel and marine biogenic emissions grows. The daytime higher atmospheric N-NO3- concentration and lower δ15N-NO3- compared with nighttime further underscore the substantial role of marine biogenic emissions.

Development of a 3-step sequential extraction method to investigate the fraction and affecting factors of 21 antibiotics in soils.

Antibiotic exist in various states after entering agricultural soil through the application of manure, including the aqueous state (I), which can be directly absorbed by plants, and the auxiliary organic extraction state (III), which is closely associated with the pseudo-permanence of antibiotics. However, effective analytical methods for extracting and affecting factors on fractions of different antibiotic states remain unclear. In this study, KCl, acetonitrile/Na2EDTA-McIlvaine buffer, and acetonitrile/water were successively used to extract states I, II, and III of 21 antibiotics in soil, and the recovery efficiency met the quantitative requirements. Random forest classification and variance partitioning analysis revealed that dissolved organic matter, pH, and organic matter were important factors affecting the recovery efficiency of antibiotic in states I, II, and III, respectively. Additionally, 65-day spiked soil experiments combined with Mantel test analysis suggested that pH, organic acids, heavy metals, and noncrystalline minerals differentially affected antibiotic type and state. Importantly, a structural equation model indicated that organic acids play a crucial role in the fraction of antibiotic states. Overall, this study reveals the factors influencing the fraction of different antibiotic states in soil, which is helpful for accurately assessing their ecological risk.

A review of disease risk prediction methods and applications in the omics era.

Risk prediction and disease prevention are the innovative care challenges of the 21st century. Apart from freeing the individual from the pain of disease, it will lead to low medical costs for society. Until very recently, risk assessments have ushered in a new era with the emergence of omics technologies, including genomics, transcriptomics, epigenomics, proteomics, and so on, which potentially advance the ability of biomarkers to aid prediction models. While risk prediction has achieved great success, there are still some challenges and limitations. We reviewed the general process of omics-based disease risk model construction and the applications in four typical diseases. Meanwhile, we highlighted the problems in current studies and explored the potential opportunities and challenges for future clinical practice.

Identifying functional subtypes of IgA nephropathy based on three machine learning algorithms and WGCNA.

BACKGROUND: IgA nephropathy (IgAN) is one of the most common primary glomerulonephritis, which is a significant cause of renal failure. At present, the classification of IgAN is often limited to pathology, and its molecular mechanism has not been established. Therefore we aim to identify subtypes of IgAN at the molecular level and explore the heterogeneity of subtypes in terms of immune cell infiltration, functional level. METHODS: Two microarray datasets (GSE116626 and GSE115857) were downloaded from GEO. Differential expression genes (DEGs) for IgAN were screened with limma. Three unsupervised clustering algorithms (hclust, PAM, and ConsensusClusterPlus) were combined to develop a single-sample subtype random forest classifier (SSRC). Functional subtypes of IgAN were defined based on functional analysis and current IgAN findings. Then the correlation between IgAN subtypes and clinical features such as eGFR and proteinuria was evaluated by using Pearson method. Subsequently, subtype heterogeneity was verified by subtype-specific modules identification based on weighted gene co-expression network analysis(WGCNA) and immune cell infiltration analysis based on CIBERSORT algorithm. RESULTS: We identified 102 DEGs as marker genes for IgAN and three functional subtypes namely: viral-hormonal, bacterial-immune and mixed type. We screened seventeen genes specific to viral hormonal type (ATF3, JUN and FOS etc.), and seven genes specific to bacterial immune type (LIF, C19orf51 and SLPI etc.). The subtype-specific genes showed significantly high correlation with proteinuria and eGFR. The WGCNA modules were in keeping with functions of the IgAN subtypes where the MEcyan module was specific to the viral-hormonal type and the MElightgreen module was specific to the bacterial-immune type. The results of immune cell infiltration revealed subtype-specific cell heterogeneity which included significant differences in T follicular helper cells, resting NK cells between viral-hormone type and control group; significant differences in eosinophils, monocytes, macrophages, mast cells and other cells between bacterial-immune type and control. CONCLUSION: In this study, we identified three functional subtypes of IgAN for the first time and specific expressed genes for each subtype. Then we constructed a subtype classifier and classify IgAN patients into specific subtypes, which may be benefit for the precise treatment of IgAN patients in future.