Constitutive activation of a nuclear-localized calcium channel complex in Medicago truncatula.

Nuclear Ca2+ oscillations allow symbiosis signaling, facilitating plant recognition of beneficial microsymbionts, nitrogen-fixing rhizobia, and nutrient-capturing arbuscular mycorrhizal fungi. Two classes of channels, DMI1 and CNGC15, in a complex on the nuclear membrane, coordinate symbiotic Ca2+ oscillations. However, the mechanism of Ca2+ signature generation is unknown. Here, we demonstrate spontaneous activation of this channel complex, through gain-of-function mutations in DMI1, leading to spontaneous nuclear Ca2+ oscillations and spontaneous nodulation, in a CNGC15-dependent manner. The mutations destabilize a hydrogen-bond or salt-bridge network between two RCK domains, with the resultant structural changes, alongside DMI1 cation permeability, activating the channel complex. This channel complex was reconstituted in human HEK293T cell lines, with the resultant calcium influx enhanced by autoactivated DMI1 and CNGC15s. Our results demonstrate the mode of activation of this nuclear channel complex, show that DMI1 and CNGC15 are sufficient to create oscillatory Ca2+ signals, and provide insights into its native mode of induction.

SLAM-Drop-seq reveals mRNA kinetic rates throughout the cell cycle.

RNA abundance is tightly regulated in eukaryotic cells by modulating the kinetic rates of RNA production, processing, and degradation. To date, little is known about time­dependent kinetic rates during dynamic processes. Here, we present SLAM­Drop­seq, a method that combines RNA metabolic labeling and alkylation of modified nucleotides in methanol­fixed cells with droplet­based sequencing to detect newly synthesized and preexisting mRNAs in single cells. As a first application, we sequenced 7280 HEK293 cells and calculated gene­specific kinetic rates during the cell cycle using the novel package Eskrate. Of the 377 robust­cycling genes that we identified, only a minor fraction is regulated solely by either dynamic transcription or degradation (6 and 4%, respectively). By contrast, the vast majority (89%) exhibit dynamically regulated transcription and degradation rates during the cell cycle. Our study thus shows that temporally regulated mRNA degradation is fundamental for the correct expression of a majority of cycling genes. SLAM­Drop­seq, combined with Eskrate, is a powerful approach to understanding the underlying mRNA kinetics of single­cell gene expression dynamics in continuous biological processes.

Duplication and sub-functionalization of flavonoid biosynthesis genes plays important role in Leguminosae root nodule symbiosis evolution.

Gene innovation plays an essential role in trait evolution. Rhizobial symbioses, the most important N2-fixing agent in agricultural systems that exists mainly in Leguminosae, is one of the most attractive evolution events. However, the gene innovations underlying Leguminosae root nodule symbiosis (RNS) remain largely unknown. Here, we investigated the gene gain event in Leguminosae RNS evolution through comprehensive phylogenomic analyses. We revealed that Leguminosae-gain genes were acquired by gene duplication and underwent a strong purifying selection. Kyoto Encyclopedia of Genes and Genomes analyses showed that the innovated genes were enriched in flavonoid biosynthesis pathways, particular downstream of chalcone synthase (CHS). Among them, Leguminosae-gain type Ⅱ chalcone isomerase (CHI) could be further divided into CHI1A and CHI1B clades, which resulted from the products of tandem duplication. Furthermore, the duplicated CHI genes exhibited exon-intron structural divergences evolved through exon/intron gain/loss and insertion/deletion. Knocking down CHI1B significantly reduced nodulation in Glycine max (soybean) and Medicago truncatula; whereas, knocking down its duplication gene CHI1A had no effect on nodulation. Therefore, Leguminosae-gain type Ⅱ CHI participated in RNS and the duplicated CHI1A and CHI1B genes exhibited RNS functional divergence. This study provides functional insights into Leguminosae-gain genetic innovation and sub-functionalization after gene duplication that contribute to the evolution and adaptation of RNS in Leguminosae.

Detection of Pilot's Mental Workload Using a Wireless EEG Headset in Airfield Traffic Pattern Tasks.

Elevated mental workload (MWL) experienced by pilots can result in increased reaction times or incorrect actions, potentially compromising flight safety. This study aims to develop a functional system to assist administrators in identifying and detecting pilots' real-time MWL and evaluate its effectiveness using designed airfield traffic pattern tasks within a realistic flight simulator. The perceived MWL in various situations was assessed and labeled using NASA Task Load Index (NASA-TLX) scores. Physiological features were then extracted using a fast Fourier transformation with 2-s sliding time windows. Feature selection was conducted by comparing the results of the Kruskal-Wallis (K-W) test and Sequential Forward Floating Selection (SFFS). The results proved that the optimal input was all PSD features. Moreover, the study analyzed the effects of electroencephalography (EEG) features from distinct brain regions and PSD changes across different MWL levels to further assess the proposed system's performance. A 10-fold cross-validation was performed on six classifiers, and the optimal accuracy of 87.57% was attained using a multi-class K-Nearest Neighbor (KNN) classifier for classifying different MWL levels. The findings indicate that the wireless headset-based system is reliable and feasible. Consequently, numerous wireless EEG device-based systems can be developed for application in diverse real-driving scenarios. Additionally, the current system contributes to future research on actual flight conditions.

Research progress on intervention effect and mechanism of protocatechuic acid on nonalcoholic fatty liver disease.

Nonalcoholic fatty liver disease (NAFLD) has become a surge burden worldwide due to its high prevalence, with complicated deterioration symptoms such as liver fibrosis and cancer. No effective drugs are available for NALFD so far. The rapid growth of clinical demand has prompted the treatment of NAFLD to become a research hotspot. Protocatechuic acid (PCA) is a natural secondary metabolite commonly found in fruits, vegetables, grains, and herbal medicine. It is also the major internal metabolites of anthocyanins and other polyphenols. In the present manuscript, food sources, metabolic absorption, and efficacy of PCA were summarized while analyzing its role in improving NAFLD, as well as the mechanism involved. The results indicated that PCA could ameliorate NAFLD by regulating glucose and lipid metabolism, oxidative stress and inflammation, gut microbiota and metabolites. It was proposed for the first time that PCA might reduce NAFLD by enhancing the energy consumption of brown adipose tissue (BAT). However, the PCA administration mode and dose for NAFLD remain inconclusive. Fresh insights into the specific molecular mechanisms are required, while clinical trials are essential in the future. This review provides new targets and reasoning for the clinical application of PCA in the prevention and treatment of NAFLD.

Corporate environmental governance scheme and investment efficiency over the course of COVID-19.

Taking the COVID-19 outbreak as the exogenous shock, we use quarterly reports of Chinese listed firms to examine whether enhanced environmental governance scheme improves corporate investment efficiency over the course of COVID-19. The results show that after the outbreak, firms with greater environmental governance scheme experience more efficient investments, with this effect being more pronounced in non-state-owned enterprises, firms unlisted as key pollution-monitoring units, and firms with higher financial constraints. The results are robust to a battery of robustness checks. These findings provide new evidence on the importance of environmental governance in reaping economic benefits and resilience during crisis times.

COVID-19 impact on firm investment-Evidence from Chinese publicly listed firms.

The COVID-19 outbreak had a significant impact on business cash flows and investment activities. This paper examined the COVID-19 impact on Chinese business investment in 3326 A-share listed quarterly financial reports, from which it was found that the negative relationship was more pronounced in the large, eastern Chinese state-owned firms. Using a propensity score matching method and difference-in-differences estimation, corporate financial flexibility was also examined, with the results indicating that high cash flexibility provided a buffer that allowed firms to better deal with adverse external shocks as the firms that had high cash flexibility were able to significantly increase their investments after the COVID-19 outbreak. Various robustness tests were conducted, all of which verified the robustness of the results. Overall, the empirical results provided evidence that the COVID-19 pandemic in China had a negative impact on Chinese listed firms, and verified the vital role of flexible financial reserves for firm survival and development during crises.

Association of sputum microbiome with clinical outcome of initial antibiotic treatment in hospitalized patients with acute exacerbations of COPD.

Identification of risk factors for antibiotic treatment failure is urgently needed in acute exacerbations of chronic obstructive pulmonary disease (AECOPD). Here we investigated the relationship between sputum microbiome and clinical outcome of choice of initial antibiotics during hospitalization of AECOPD patients. Sputum samples of 41 AECOPD patients and 26 healthy controls were collected from Guangzhou Medical University, China. Samples were processed for 16S rRNA gene-based microbiome profiling. Thirty patients recovered with initial antibiotic treatment (antibiotic success or AS), while 11 patients showed poor outcome (antibiotic failure or AF). Substantial differences in microbiome were observed in AF versus AS patients and healthy controls. There was significantly decreased alpha diversity and increased relative abundances of Pseudomonas, Achromobacter, Stenotrophomonas and Ralstonia in AF patients. Conversely, Prevotella, Peptostreptococcus, Leptotrichia and Selenomonas were depleted. The prevalence of Selenomonas was markedly reduced in AF versus AS patients (9.1 % versus 60.0 %, P = 0.004). The AF patients with similar microbiome profiles in general responded well to the same new antibiotics in the adjusted therapy, indicating sputum microbiome may help guide the adjustment of antibiotics. Random forest analysis identified five microbiome operational taxonomic units together with C-reactive protein, procalcitonin and blood neutrophil count showing best predictability for antibiotic treatment outcome (area under curve 0.885). Functional inference revealed an enrichment of microbial genes in xenobiotic metabolism and antimicrobial resistance in AF patients, whereas genes in DNA repair and amino acid metabolism were depleted. Sputum microbiome may determine the clinical outcome of initial antibiotic treatment and be considered in the risk management of antibiotics in AECOPD.

A Single-Cell Transcriptome Atlas of the Mouse Glomerulus.

Background Three different cell types constitute the glomerular filter: mesangial cells, endothelial cells, and podocytes. However, to what extent cellular heterogeneity exists within healthy glomerular cell populations remains unknown.Methods We used nanodroplet-based highly parallel transcriptional profiling to characterize the cellular content of purified wild-type mouse glomeruli.Results Unsupervised clustering of nearly 13,000 single-cell transcriptomes identified the three known glomerular cell types. We provide a comprehensive online atlas of gene expression in glomerular cells that can be queried and visualized using an interactive and freely available database. Novel marker genes for all glomerular cell types were identified and supported by immunohistochemistry images obtained from the Human Protein Atlas. Subclustering of endothelial cells revealed a subset of endothelium that expressed marker genes related to endothelial proliferation. By comparison, the podocyte population appeared more homogeneous but contained three smaller, previously unknown subpopulations.Conclusions Our study comprehensively characterized gene expression in individual glomerular cells and sets the stage for the dissection of glomerular function at the single-cell level in health and disease.

Synergistic action of master transcription factors controls epithelial-to-mesenchymal transition.

Epithelial-to-mesenchymal transition (EMT) is a complex multistep process in which phenotype switches are mediated by a network of transcription factors (TFs). Systematic characterization of all dynamic TFs controlling EMT state transitions, especially for the intermediate partial-EMT state, represents a highly relevant yet largely unexplored task. Here, we performed a computational analysis that integrated time-course EMT transcriptomic data with public cistromic data and identified three synergistic master TFs (ETS2, HNF4A and JUNB) that regulate the transition through the partial-EMT state. Overexpression of these regulators predicted a poor clinical outcome, and their elimination readily abolished TGF-ß-induced EMT. Importantly, these factors utilized a clique motif, physically interact and their cumulative binding generally characterized EMT-associated genes. Furthermore, analyses of H3K27ac ChIP-seq data revealed that ETS2, HNF4A and JUNB are associated with super-enhancers and the administration of BRD4 inhibitor readily abolished TGF-ß-induced EMT. These findings have implications for systematic discovery of master EMT regulators and super-enhancers as novel targets for controlling metastasis.

[Analysis of Theory and Performance of Multi Layer Graphene Nanoribbons Photodetector].

Multilayer graphene, with wide absorption spectrum and unique photoelectric properties, is an ideal material to make the next generation of photoelectric detector. Taking graphene interband tunneling theory as the foundation, a photoelectric detector model with the structure of multilayer graphene nanoribbons was proposed. Nanoribbons which contacted with source and drain electrode at the end were sandwiched between the semiconductor substrate and the top and back gate. Using this model, a photoelectric conversion mechanism of multilayer graphene nanoribbon detector was established. It discussed the working principle of the detector at different top gate voltage, studied the relationship between the source-drain current and the incident light energy, researched the influence of the bias voltage, the length of depletion and the values of band gap on the dark current, and analyzed the change of detector responsibility and detectivity with the incident light energy under the different parameters. The results show that, the responsibility of detector increases with the layers of nanoribbons, and are affected by the band gap, the length of depletion and the bias voltage. The maximum responsibility up to 10(3) A·W(-1); By limiting on the top gate voltage, the band gap and other variables can control the dark current of system and increase the detectivity, the detectivity up to a maximum value of 10(9) cm Hz(1/2)·W(-1). The structure of multilayer graphene nanoribbons can enhance the absorption of the incident light, improve the sensitivity of the detector and the detection capability of weak light, and realize the detection from THz to far infrared wavelength of incident light. The detection performance is far better than that of many quantum structures and narrow-band semiconductor structure of photoelectric detector.

Analyzing the heterogenous effects of factors on high-range speeding likelihood of taxi speeders: Does explainable deep learning provides more insights than random parameter approach?

The random parameters Generalized Linear Model (GLM) is frequently used to model speeding characteristics and capture the heterogenous effects of factors. However, this statistical approach is seldom employed for prediction and generalization due to the challenge of transferring its predefined errors. Recently, the emergence of explainable AI techniques has illuminated a new path for analyzing factors associated with risky driving behaviors. Despite this, there remains a gap that comparing results from machine and deep learning (ML/DL) approaches with those from random parameters GLM. This study aims to apply the random parameter GLM and explainable deep learning to evaluate the heterogenous effects of factors on the taxis' high-range speeding likelihood. Initially, a Beta GLM with random parameters (BGLM-RP) is developed to model the high-range speeding likelihood among taxi drivers. Additionally, XGBoost, a simple convolutional neural network (Simple-CNN), a deeper CNN (DCNN), and a deeper CNN with self-attention (DCNN-SA) are developed. The quantified explanations and illustrations of the factors' heterogenous effects from ML/DL models are derived from pseudo coefficients by decomposing factors' SHapley Additive exPlanations (SHAP) values. All the developed statistical, ML, and DL models are compared in terms of mean absolute errors and mean square errors on testing and full data. Results show that DCNN-SA excels in prediction on testing data, indicating its superior generalization capabilities, while BGLM-RP outperforms other models on full data. The DCNN-SA can reveal the heterogenous effects of factors for both in-sample and out-of-sample data, which is not possible for the random parameter GLM. However, BGLM-RP can reveal larger magnitudes of the factors' heterogenous effects for in-sample data. The signs and significances are identical between the varying coefficients from BGLM-RP and the pseudo coefficients from the ML/DL models, demonstrating the validity and rationale of using the proposed explanation framework to quantify the factors' effects in ML/DL models. The study also discusses the contributions of various factors to the high-range speeding likelihood of taxi drivers.

Who might encounter hard-braking while speeding? Analysis for regular speeders using low-frequency taxi trajectories on arterial roads and explainable AI.

Regular speeders are those who commit speeding recidivism during a period. Among their speeding behaviors, some occurring in specific scenarios may cause more hazards to road users. Therefore, there is a need to evaluate the driving risks if the regular speeders have different speeding propensities. This study considers speeding-related hard-braking events (SHEs) as a safety surrogate measure and recognizes the regular speeders who encounter at least one SHEs during the study period as risky individuals. To identify speeding behaviors and hard-braking events from low-frequency GPS trajectories, we compare the average travel speed between pairwise adjacent GPS points to the posted speed limit and examine the speed curve and the corresponding travel distance between these GPS points, respectively. Thereafter, a logistic model, XGBoost, and three 1D Convolutional Neural Networks (CNNs) including AlexNet CNN, Mini-AlexNet CNN, and Simple CNN are respectively developed to recognize the regular speeders who encountered SHEs based on their speeding propensities. The proposed Mini-AlexNet CNN achieves a global F1-score of 91% and recall of 90% on the testing data, which are superior to other models. Further, the study uses the Shapley Additive exPlanation (SHAP) framework to visually interpret the contribution of speeding propensities on SHE likelihood. It is found that speeding by 50% or greater for no more than 285 m is the most dangerous kind among all the speeding behaviors. Speeding on roads without bicycle lanes or on roads with roadside parking and excessive accesses increases the probability of encountering SHEs. Based on the analyses, we put forward tailored recommendations that aim to restrict hazard-related speeding behaviors rather than speeding behaviors of all kinds.

Comparison of the upper and lower airway microbiome in early postoperative lung transplant recipients.

The upper and lower respiratory tract may share microbiome because they are directly continuous, and the nasal microbiome contributes partially to the composition of the lung microbiome. But little is known about the upper and lower airway microbiome of early postoperative lung transplant recipients (LTRs). Using 16S rRNA gene sequencing, we compared paired nasal swab (NS) and bronchoalveolar lavage fluid (BALF) microbiome from 17 early postoperative LTRs. The microbiome between the two compartments were significantly different in Shannon diversity and beta diversity. Four and eight core NS-associated and BALF-associated microbiome were identified, respectively. NS samples harbored more Corynebacterium, Acinetobacter, and Pseudomonas, while BALF contained more Ralstonia, Stenotrophomonas, Enterococcus, and Pedobacter. The within-subject dissimilarity was higher than the between-subject dissimilarity, indicating a greater impact of sampling sites than sampling individuals on microbial difference. There were both difference and homogeneity between NS and BALF microbiome in early postoperative LTRs. High levels of pathogens were detected in both samples, suggesting that both of them can reflect the diseases characteristics of transplanted lung. The differences between upper and lower airway microbiome mainly come from sampling sites instead of sampling individuals. IMPORTANCE: Lung transplantation is the only therapeutic option for patients with end-stage lung disease, but its outcome is much worse than other solid organ transplants. Little is known about the NS and BALF microbiome of early postoperative LTRs. Here, we compared paired samples of the nasal and lung microbiome from 17 early postoperative LTRs and showed both difference and homogeneity between the two samples. Most of the "core" microbiome in both NS and BALF samples were recognized respiratory pathogens, suggesting that both samples can reflect the diseases characteristics of transplanted lung. We also found that the differences between upper and lower airway microbiome in early postoperative LTRs mainly come from sampling sites instead of sampling individuals.

The gut microbiota-brain axis in neurological disorders.

Previous studies have shown a bidirectional communication between human gut microbiota and the brain, known as the microbiota-gut-brain axis (MGBA). The MGBA influences the host's nervous system development, emotional regulation, and cognitive function through neurotransmitters, immune modulation, and metabolic pathways. Factors like diet, lifestyle, genetics, and environment shape the gut microbiota composition together. Most research have explored how gut microbiota regulates host physiology and its potential in preventing and treating neurological disorders. However, the individual heterogeneity of gut microbiota, strains playing a dominant role in neurological diseases, and the interactions of these microbial metabolites with the central/peripheral nervous systems still need exploration. This review summarizes the potential role of gut microbiota in driving neurodevelopmental disorders (autism spectrum disorder and attention deficit/hyperactivity disorder), neurodegenerative diseases (Alzheimer's and Parkinson's disease), and mood disorders (anxiety and depression) in recent years and discusses the current clinical and preclinical gut microbe-based interventions, including dietary intervention, probiotics, prebiotics, and fecal microbiota transplantation. It also puts forward the current insufficient research on gut microbiota in neurological disorders and provides a framework for further research on neurological disorders.

Dietary inflammation influences the prevalence of cardiovascular diseases in prediabetes and diabetes patients: findings from the National Health and Nutrition Examination Survey (NHANES 2001-2018).

Prediabetes is an early phase before diabetes. Diabetes and dietary inflammation are two crucial factors that are strongly associated with cardiovascular diseases (CVDs). Dietary interventions slowed the progression of diabetes and CVD. However, the associations between CVDs and dietary inflammation in different stages of pathoglycaemia have not been investigated. To explore the effect of a proinflammatory diet on CVD incidence at different stages of diabetes, NHANES (2001-2018) data were collected and analysed. A total of 3137 CVD patients with a comparable non-CVD group (n = 3137) were enrolled after propensity score matching (PSM) analysis. These patients were subsequently categorized into three subgroups: those with diabetes (n = 3043), those with prediabetes (n = 1099) and those with normoglycemia (n = 2132). The DII (Dietary inflammatory index) is a risk factor for CVD, both in overall individuals and in each subgroup of population-based information. In diabetic individuals, the odds ratios (ORs) (95% CIs) of CVD incidence for the DII were 1.10 (1.05, 1.15) and 1.08 (1.03, 1.13) according to the crude and adjusted models, respectively. For individuals with prediabetes, the ORs (95% CIs) of CVD risk for DII were 1.05 (0.97, 1.14) and 1.11 (1.01, 1.22) according to the crude and adjusted models, respectively. After adjusting for population-based information and hypertension status, the DII appeared to have the highest OR for individuals with prediabetes, and no significant association was found between the DII score and CVD risk in the normoglycemia group. Moreover, the OR of CVD for DII in the uncontrolled diabetes group was 1.06 (0.98, 1.16)*. These results suggest that the DII is more closely associated with the risk of CVDs in prediabetic and diabetic populations, and we should pay more attention to diet control before a person develops diabetes to prevent CVD progression.

Research development on gut microbiota and vulnerable atherosclerotic plaque.

The relationship between gut microbiota and its metabolites with cardiovascular disease (CVD) has been proven. In this review, we aim to conclude the potential mechanism of gut microbiota and its metabolites on inducing the formation of vulnerable atherosclerotic plaque, and to discuss the effect of intestinal metabolites, including trimethylamine-N-oxide (TMAO), lipopolysaccharide (LPS), phenylacetylglutamine (PAG), short-chain fatty acids (SCFAs) on plaque stability. Finally, we include the impact of gut microbiota and its metabolites on plaque stability, to propose a new therapeutic direction for coronary heart disease. Gut microbiota regulation intervenes the progress of arteriosclerosis, especially on coronary atherosclerosis, by avoiding or reducing the formation of vulnerable plaque, to lower the morbidity rate of myocardial infarction.

The nasal microbiota is a potential diagnostic biomarker for sepsis in critical care units.

This study aimed to characterize the composition of intestinal and nasal microbiota in septic patients and identify potential microbial biomarkers for diagnosis. A total of 157 subjects, including 89 with sepsis, were enrolled from the affiliated hospital. Nasal swabs and fecal specimens were collected from septic and non-septic patients in the intensive care unit (ICU) and Department of Respiratory and Critical Care Medicine. DNA was extracted, and the V4 region of the 16S rRNA gene was amplified and sequenced using Illumina technology. Bioinformatics analysis, statistical processing, and machine learning techniques were employed to differentiate between septic and non-septic patients. The nasal microbiota of septic patients exhibited significantly lower community richness (P = 0.002) and distinct compositions (P = 0.001) compared to non-septic patients. Corynebacterium, Staphylococcus, Acinetobacter, and Pseudomonas were identified as enriched genera in the nasal microbiota of septic patients. The constructed machine learning model achieved an area under the curve (AUC) of 89.08, indicating its efficacy in differentiating septic and non-septic patients. Importantly, model validation demonstrated the effectiveness of the nasal microecological diagnosis prediction model with an AUC of 84.79, while the gut microecological diagnosis prediction model had poor predictive performance (AUC = 49.24). The nasal microbiota of ICU patients effectively distinguishes sepsis from non-septic cases and outperforms the gut microbiota. These findings have implications for the development of diagnostic strategies and advancements in critical care medicine.IMPORTANCEThe important clinical significance of this study is that it compared the intestinal and nasal microbiota of sepsis with non-sepsis patients and determined that the nasal microbiota is more effective than the intestinal microbiota in distinguishing patients with sepsis from those without sepsis, based on the difference in the lines of nasal specimens collected.

Cellular and molecular basis of symbiotic nodule development.

Root nodule development plays a vital role in establishing the mutualistic relationship between legumes and nitrogen-fixing rhizobia. Two primary processes are involved in nodule development: formative cell divisions in the root cortex and the subsequent differentiation of nodule cells. The first process involves the mitotic reactivation of differentiated root cortex cells to form nodule primordium after perceiving symbiotic signals. The second process enables the nascent nodule primordium cells to develop into various cell types, leading to the creation of a functional nodule capable of supporting nitrogen fixation. Thus, both division and differentiation of nodule cells are crucial for root nodule development. This review provides an overview of the most recent advancements in comprehending the cellular and molecular mechanisms underlying symbiotic nodule development in legumes.

Urinary peptidome analysis in CKD and IgA nephropathy.

Background: Chronic kidney disease (CKD) has emerged as a significant challenge to human health and economic stability in aging societies worldwide. Current clinical practice strategies remain insufficient for the early identification of kidney dysfunction, and the differential diagnosis of immunoglobulin A nephropathy (IgAN) predominantly relies on invasive kidney biopsy procedures. Methods: First, we assessed a case-control cohort to obtain urine samples from healthy controls and biopsy-confirmed CKD patients. Matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) was applied to detect urinary peptide and then these urinary peptide profiles were used to construct diagnostic models to distinguish CKD patients from controls and identify IgAN patients from other nephropathy patients. Furthermore, we assessed the robustness of the diagnostic models and their reproducibility by applying different algorithms. Results: A rapid and accurate working platform for detecting CKD and its IgAN subtype based on urinary peptide pattern detected by MALDI-TOF MS was established. Naturally occurring urinary peptide profiles were used to construct a diagnostic model to distinguish CKD patients from controls and identify IgAN patients from other nephropathy patients. The performance of several algorithms was assessed and demonstrated that the robustness of the diagnostic models as well as their reproducibility were satisfactory. Conclusions: The present findings suggest that the CKD-related and IgAN-specific urinary peptides discovered facilitate precise identification of CKD and its IgAN subtype, offering a dependable framework for screening conditions linked to renal dysfunction. This will aid in comprehending the pathogenesis of nephropathy and identifying potential protein targets for the clinical management of nephropathy.