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Additive engineering, with its excellent ability to passivate bulk or surface perovskite defects, has become a common strategy to improve the performance and stability of perovskite solar cells (PVSCs). Among the various additives reported so far, ammonium salts are considered an important branch. It is worth noting that although both ammonium-based additives (R-NH3 +) and amine-based additives (R-NH2) are derivatives of ammonia (NH3), the functions of the two can be easily confused due to their structural similarities. Moreover, there is no comprehensive comparative analysis of them in the literature. Here, the differences between phenethylammonium iodide (PEA+) and phenethylamine (PEA) additives are revealed experimentally and theoretically. The results clearly show that PEA outperforms PEA+ in terms of device performance and stability based on the following three factors: i) PEA's defect passivation capability is superior to that of PEA+; ii) PEA has better hydrophobicity to hinder water ingress; and iii) PEA completely improves the stability of PVSCs by enhancing thermal stability and inhibiting iodide migration in perovskite more effectively than PEA+. As a result, the power conversion efficiency (PCE) of the inverted methylammonium triiodide (MAPbI3) device using PEA increases by ≈15% to over 21%. More importantly, this device exhibits greater ability to prevent water invasion, thermal-induce degradation, and inhibit iodide ion migration, resulting in better long-term stability.

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Background: An increasing body of evidence suggests that neuroinflammation is one of the key drivers of late-onset Alzheimer's disease (LOAD) pathology. Due to the increased permeability of the blood-brain barrier (BBB) in older adults, peripheral plasma proteins can infiltrate the central nervous system (CNS) and drive neuroinflammation through interactions with neurons and glial cells. Because these inflammatory factors are heritable, a greater understanding of their genetic relationship with LOAD could identify new biomarkers that contribute to LOAD pathology or offer protection against it. Methods: We used a genome-wide association study (GWAS) of 90 different plasma proteins (n = 17,747) to create polygenic scores (PGSs) in an independent discovery (cases = 1,852 and controls = 1,990) and replication (cases = 799 and controls = 778) cohort. Multivariate logistic regression was used to associate the plasma protein PGSs with LOAD diagnosis while controlling for age, sex, principal components 1-2, and the number of APOE-e4 alleles as covariates. After meta-analyzing the PGS-LOAD associations between the two cohorts, we then performed a two-sample Mendelian randomization (MR) analysis using the summary statistics of significant plasma protein level PGSs in the meta-analysis as an exposure, and a GWAS of clinically diagnosed LOAD (cases = 21,982, controls = 41,944) as an outcome to explore possible causal relationships between the two. Results: We identified four plasma protein level PGSs that were significantly associated (FDR-adjusted p < 0.05) with LOAD in a meta-analysis of the discovery and replication cohorts: CX3CL1, hepatocyte growth factor (HGF), TIE2, and matrix metalloproteinase-3 (MMP-3). When these four plasma proteins were used as exposures in MR with LOAD liability as the outcome, plasma levels of HGF were inferred to have a negative causal relationship with the disease when single-nucleotide polymorphisms (SNPs) used as instrumental variables were not restricted to cis-variants (OR/95%CI = 0.945/0.906-0.984, p = 0.005). Conclusion: Our results show that plasma HGF has a negative causal relationship with LOAD liability that is driven by pleiotropic SNPs possibly involved in other pathways. These findings suggest a low transferability between PGS and MR approaches, and future research should explore ways in which LOAD and the plasma proteome may interact.

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OBJECTIVE: Wound therapies utilizing gene delivery to the skin offer considerable promise owing to their localized treatment benefits and straightforward application. This study investigated the impact of skin electroporation of CYP1A1 shRNA lentiviral particles on diabetic wound healing in a streptozotocin (STZ)-induced rat model. METHODS: Male Sprague Dawley (SD) rats were made diabetic by injecting STZ and subsequently creating foot skin wounds. The rats were randomly divided into four groups: normal, diabetic foot ulcers (DFU), DFU + control shRNA (electroporation of control shRNA lentiviral particles), and DFU + CYP1A1 shRNA (electroporation of CYP1A1 shRNA lentiviral particles). Wound healing progress was monitored at multiple time points (0, 1, 3, 5, 7, 10, 14 days). On day 14, wound tissue specimens were collected for histological examination. Wound samples collected at days 7 and 14 were used for gene expression analysis via qRT-PCR, assessment of CYP1A1 protein levels using western blotting, and evaluation of oxidative stress markers. RESULTS: Treatment with CYP1A1 shRNA significantly enhanced diabetic wound healing rates compared to untreated controls over the observation period. Histological analysis revealed improved wound characteristics in the CYP1A1 shRNA-treated group, including enhanced epithelial regeneration, reduced inflammation, and increased collagen deposition, indicative of improved tissue repair. Furthermore, suppression of CYP1A1 corresponded with decreased expression levels of pro-inflammatory cytokines (interleukin-1ß, tumor necrosis factor-α, and interleukin-6) and diminished oxidative stress markers (malondialdehyde, superoxide dismutase) within wound tissues. CONCLUSION: Targeted suppression of CYP1A1 represents a promising therapeutic strategy to enhance diabetic wound healing by modulating inflammation and oxidative stress.

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Alzheimer's disease (AD) is a complex multifaceted disease. Recently approved anti-amyloid monoclonal antibodies slow disease progression by approximately 30%, and combination therapy appears necessary to prevent the onset of AD or produce greater slowing of cognitive and functional decline. Combination therapies may address core features, non-specific co-pathology commonly occurring in patients with AD (e.g., inflammation), or non-AD pathologies that may co-occur with AD (e.g., α-synuclein). Combination therapies may be advanced through co-development of more than one new molecular entity or through add-on strategies including an approved agent plus a new molecular entity. Addressing add-on combination therapy is currently urgent since patients on anti-amyloid monoclonal antibodies may be included in clinical trials for experimental agents. Phase 1 information must be generated for each agent in combination drug development. Phase 2 and Phase 3 of add-on therapies may contrast the new molecular entity, the approved agent as standard of care, and the combination. More complex development programs including standard or modified combinatorial designs are required for co-development of two or more new molecular entities. Biomarkers are markedly affected by anti-amyloid monoclonal antibodies, and these effects must be anticipated in add-on trials. Examining target engagement biomarkers and comparing the magnitude and sequence of biomarker changes in those receiving more than one therapy, compared with those on monotherapy, may be informative. Using network-based medicine approaches, computational strategies may identify rational combinations using disease and drug effect network mapping.

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Recent GWASs have demonstrated that comorbid disorders share genetic liabilities. But whether and how these shared liabilities can be used for the classification and differentiation of comorbid disorders remains unclear. In this study, we use polygenic risk scores (PRSs) estimated from 42 comorbid traits and the deep neural networks (DNN) architecture to classify and differentiate schizophrenia (SCZ), bipolar disorder (BIP) and major depressive disorder (MDD). Multiple PRSs were obtained for individuals from the schizophrenia (SCZ) (cases = 6,317, controls = 7,240), bipolar disorder (BIP) (cases = 2,634, controls 4,425) and major depressive disorder (MDD) (cases = 1,704, controls = 3,357) datasets, and classification models were constructed with and without the inclusion of PRSs of the target (SCZ, BIP or MDD). Models with the inclusion of target PRSs performed well as expected. Surprisingly, we found that SCZ could be classified with only the PRSs from 35 comorbid traits (not including the target SCZ and directly related traits) (accuracy 0.760 ± 0.007, AUC 0.843 ± 0.005). Similar results were obtained for BIP (33 traits, accuracy 0.768 ± 0.007, AUC 0.848 ± 0.009), and MDD (36 traits, accuracy 0.794 ± 0.010, AUC 0.869 ± 0.004). Furthermore, these PRSs from comorbid traits alone could effectively differentiate unaffected controls, SCZ, BIP, and MDD patients (average categorical accuracy 0.861 ± 0.003, average AUC 0.961 ± 0.041). These results suggest that the shared liabilities from comorbid traits alone may be sufficient to classify SCZ, BIP and MDD. More importantly, these results imply that a data-driven and objective diagnosis and differentiation of SCZ, BIP and MDD may be feasible.

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Recent advances in microphysiological systems (MPS), also known as organs-on-a-chip (OoC), enable the recapitulation of more complex organ and tissue functions on a smaller scale in vitro. MPS therefore provide the potential to better understand human diseases and physiology. To date, numerous MPS platforms have been developed for various tissues and organs, including the heart, liver, kidney, blood vessels, muscle, and adipose tissue. However, only a few studies have explored using MPS platforms to unravel the effects of aging on human physiology and the pathogenesis of age-related diseases. Age is one of the risk factors for many diseases, and enormous interest has been devoted to aging research. As such, a human MPS aging model could provide a more predictive tool to understand the molecular and cellular mechanisms underlying human aging and age-related diseases. These models can also be used to evaluate preclinical drugs for age-related diseases and translate them into clinical settings. Here, we provide a review on the application of MPS in aging research. First, we offer an overview of the molecular, cellular, and physiological changes with age in several tissues or organs. Next, we discuss previous aging models and the current state of MPS for studying human aging and age-related conditions. Lastly, we address the limitations of current MPS and present future directions on the potential of MPS platforms for human aging research.

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Two monoclonal antibodies (mAbs), aducanumab and lecanemab, have received accelerated approval from the US FDA for initiation of treatment in early Alzheimer's disease patients who have proven ß-amyloid pathology (Aß). One of these, lecanemab, has subsequently received full approval and other monoclonal antibodies are poised for positive review and approval. Anti-amyloid mAbs share the feature of producing a marked reduction in total brain Aß revealed by amyloid positron emission tomography. Trials associated with slowing of cognitive decline have achieved a reduction in measurable plaque Aß in the range of 15-25 centiloids; trials of agents that did not reach this threshold were not associated with cognitive benefit. mAbs have differences in terms of titration schedules, MRI monitoring schedules for amyloid-related imaging abnormalities (ARIA), and continuing versus interrupted therapy. The approximate 30% slowing of decline observed with mAbs is clinically meaningful in terms of extended cognitive integrity and delay of onset of the more severe dementia phases of Alzheimer's disease. Approval of these agents initiates a new era in Alzheimer's disease therapeutics with disease-modifying properties. Further advances are needed, i.e. greater efficacy, improved safety, enhanced convenience, and better understanding of ill-understood observations such as brain volume loss.

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Purpose: To develop a population pharmacokinetic model describing teicoplanin concentrations in patients hospitalized in intensive care unit (ICU) and to perform Monte Carlo simulations to provide detailed dosing regimens of teicoplanin. Methods: This single-center, prospective, observational study was conducted on 151 patients in ICU with 347 plasma samples. The population pharmacokinetics model was established and various covariates were evaluated. The probability of target attainment (PTA) of various proposal dosing regimens was calculated by Monte Carlo simulations. Results: The two-compartment model adequately described teicoplanin concentration-time data. The estimated glomerular filtration rate (eGFR) associated with systemic clearance (CL) was the only covariate included in the final model. The estimate of CL was 0.838 L/h, with the eGFR adjustment factor of 0.00823. The volume of the central compartment (Vc), inter-compartmental clearance (Q) and volumes of the peripheral compartments (Vp) were 14.4 L, 3.08 L/h and 51.6 L, respectively. The simulations revealed that the standard dosage regimen was only sufficient for the patients with severe renal dysfunction (eGFR ≤ 30 mL/min/1.73 m2) to attain target trough concentration (Cmin, PTA 52.8%). When eGFR > 30 mL/min/1.73 m2, increasing dose and the administration times of loading doses were the preferred options to achieve target Cmin based on the renal function and types of infection. Conclusion: The most commonly used standard dosage regimen was insufficient for all ICU patients. Our study provided detailed dosing regimens of teicoplanin stratified by eGFR and types of infection for ICU patients.

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Background: Alzheimer's disease (AD) is a common neurodegenerative disease in the elderly population, with genetic factors playing an important role. A considerable proportion of elderly people carry a high genetic AD risk but evade AD. On the other hand, some individuals with a low risk for AD eventually develop AD. We hypothesized that unknown counterfactors might be involved in reversing the polygenic risk scores (PRS) prediction, which might provide insights into AD pathogenesis, prevention, and early clinical intervention. Methods: We built a novel computational framework to identify genetically-regulated pathways (GRPa) using PRS-based stratification for each cohort. We curated two AD cohorts with genotyping data; the discovery and the replication dataset include 2722 and 2492 individuals, respectively. First, we calculated the optimized PRS model based on the three latest AD GWAS summary statistics for each cohort. Then, we sub-grouped the individuals by their PRS and clinical diagnosis into groups such as cognitively normal (CN) with high PRS for AD (resilient group), AD cases with low PRS (susceptible group), and AD/CNs participants with similar PRS backgrounds. Lastly, we imputed the individual genetically-regulated expression (GReX) and identified the differential GRPas between subgroups with gene-set enrichment analysis and gene-set variational analysis in 2 models with and without the effect of APOE. Results: For each subgroup, we conducted the same procedures in both the discovery and replication datasets across three PRS models for comparison. In Model 1 with the APOE region, we identified well-known AD-related pathways, including amyloid-beta clearance, tau protein binding, and astrocytes response to oxidative stress. In Model 2 without the APOE region, synapse function, microglia function, histidine metabolism, and thiolester hydrolase activity were significant, suggesting that they are pathways independent of the effect of APOE. Finally, our GRPa-PRS method reduces the false discovery rate in detecting differential pathways compared to another variants-based pathway PRS method. Conclusions: We developed a framework, GRPa-PRS, to systematically explore the differential GRPas among individuals stratified by their estimated PRS. The GReX-level comparison among those groups unveiled new insights into the pathways associated with AD risk and resilience. Our framework can be extended to other polygenic complex diseases.

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Cancer stem cells (CSCs), also known as tumor-initiating cells (TICs), are a subset of tumor cells that persist within tumors as a distinct population. They drive tumor initiation, relapse, and metastasis through self-renewal and differentiation into multiple cell types, similar to typical stem cell processes. Despite their importance, the morphological features of CSCs have been poorly understood. Recent advances in artificial intelligence (AI) technology have provided automated recognition of biological images of various stem cells, including CSCs, leading to a surge in deep learning research in this field. This mini-review explores the emerging trend of deep learning research in the field of CSCs. It introduces diverse convolutional neural network (CNN)-based deep learning models for stem cell research and discusses the application of deep learning for CSC research. Finally, it provides perspectives and limitations in the field of deep learning-based stem cell research.

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Particle media are widely used in engineering and greatly influence the performance of engineering materials. Asphalt mixtures are multi-phase composite materials, of which coarse aggregates account for more than 60%. These coarse aggregates form a stable structure to transfer and disperse traffic loads. Therefore, knowing how to adjust the structural composition of coarse aggregates to optimize their performance is the key to optimize the performance of asphalt mixtures. In this study, the effects of different roughness and different sizes on the interlocking force and contact force of coarse aggregates were investigated through means of simulation (DEM), and then the formation-evolution mechanism of the coarse aggregate structure and the role of different sizes of aggregates in the coarse aggregate structure were analyzed. Subsequently, the optimal ratio of coarse aggregates was explored through indoor tests, and finally, the gradation of asphalt mixture based on the optimization of fine structure was formed and verified through indoor tests. The results showed that the major model can effectively reveal the role of different types of aggregates in the fine structure and the relationship between the strength of contact forces between them and clarify that the strength of the fine structure increases with the increase in aggregate roughness. Hence, the coarse aggregate structure can be regarded as a contact force transmission system composed of some strong and sub-strong contact forces. Their formation-evolution mechanism can be regarded as a process of the formation of strong and sub-strong contact forces and the transformation from sub-strong contact force to strong contact force. Moreover, the dynamic stability of the optimized graded asphalt mixture was increased by 30%, and the fracture toughness was increased by 26%.

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Aggregate-asphalt adhesion plays an important role in the water stability of asphalt concrete. In various test standards of different countries, it is evaluated via the subjective judgment of testers using the boiling water test. The subjective judgment in the test method is detrimental to the accuracy of the adhesion evaluation. However, there is no quantitative evaluation method for the aggregate-asphalt adhesion in existing studies. Moreover, the effects of aggregate shape on adhesion are also not discussed and stipulated. Hence, an innovative method based on the Chinese boiling water test and image processing technique is put forward to quantificationally evaluate the aggregate-asphalt adhesion. Moreover, the effects of aggregate shapes on adhesion are also investigated via the proposed method from a view of aspect ratio and homogeneity. Results show that the peeling of the asphalt membrane on the aggregate surface is more serious as the complexity of the aggregate shape increases after the boiling water tests, while the effect degree gradually decreases. The effect of aspect ratio on the peeling status of asphalt membrane is lower than that of aggregate homogeneity.

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Melanoma is the most invasive and deadly skin cancer, which causes most of the deaths from skin cancer. It has been demonstrated that the mechanical properties of tumor tissue are significantly altered. However, data about characterizing the mechanical properties of in vivo melanoma tissue are extremely scarce. In addition, the viscoelastic or viscous properties of melanoma tissue are rarely reported. In this study, we measured and quantitated the viscoelastic properties of human melanoma tissues based on the stress relaxation test, using the indentation-based mechanical analyzer that we developed previously. The melanoma tissues from eight patients of different ages (57-95), genders (male and female patients), races (White and Asian), and sites (nose, arm, shoulder, and chest) were excised and tested. The results showed that the elastic property (i.e., shear modulus) of melanoma tissue was elevated compared to normal tissue, while the viscous property (i.e., relaxation time) was reduced. Moreover, the tissue thickness had a significant impact on the viscoelastic properties, probably due to the amount of the adipose layer. Our findings provide new insights into the role of the viscous and elastic properties of melanoma cell mechanics, which may be implicated in the disease state and progression.

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Polymyxin B (PB) is a dose-dependent drug used to treat multidrug-resistantgram-negative bacteria, for which a suitable method is needed to determine clinical samples. A simple, economical, and efficient high-performance liquid chromatography-mass spectrometry (HPLC-MS/MS) method was developed and validated for polymyxin B1 (PB1), polymyxin B1-Ile (PB1-I), polymyxin B2 (PB2), and polymyxin B3 (PB3) in human plasma. Chromatographic column was Waters BEH C18 column (2.1 × 50 mm, 1.7 µm). Phase A was water with 0.2% formic acid (FA), and phase B was acetonitrile containing 0.2% FA. The elution method is gradient elutio. The total analysis time was 5 min. The pretreatment method involved protein precipitation using acetonitrile containing 0.2% trifluoroacetic acid and 0.1% FA as the precipitant. The recovery rate was 92-99%. The total quantity of PB1 and PB1-I was measured in the linear range of 100-8000 ng/mL. Simultaneously, the total amounts of PB2 and PB3 were measured in the linear range of 11.9-948.5 ng/mL. This validated method was successfully applied to the pharmacokinetics of PB in critically ill patients.

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Introduction: Cardiac surgery-associated acute kidney injury (CSA-AKI) is a common hospital-acquired AKI that carries a grave disease burden. Recently, gut-kidney crosstalk has greatly changed our understanding of the pathogenesis of kidney diseases. However, the relationship between gut microbial dysbiosis and CSA-AKI remains unclear. The purpose of this study was to investigate the possible contributions of gut microbiota alterations in CSA-AKI patients. Methods: Patients undergoing cardiac surgery were enrolled and divided into acute kidney injury (AKI) and Non_AKI groups. Faecal samples were collected before the operation. Shotgun metagenomic sequencing was performed to identify the taxonomic composition of the intestinal microbiome. All groups were statistically compared with alpha- and beta-diversity analysis, and linear discriminant analysis effect size (LEfSe) analysis was performed. Results: A total of 70 individuals comprising 35 AKI and 35 Non_AKI were enrolled in the study. There was no significant difference between the AKI and Non_AKI groups with respect to the alpha-and beta-diversity of the Shannon index, Simpson or Chao1 index values except with respect to functional pathways (p < 0.05). However, the relative abundance of top 10 gut microbiota in CSA-AKI was different from the Non_AKI group. Interestingly, both LEfSe and multivariate analysis confirmed that the species Escherichia coli, Rothia mucilaginosa, and Clostridium innocuum were associated with CSA-AKI. Moreover, correlation heat map indicated that altered pathways and disrupted function could be attributed to disturbances of gut microbiota involving Escherichia coli. Conclusion: Dysbiosis of the intestinal microbiota in preoperative stool affects susceptibility to CSA-AKI, indicating the crucial role of key microbial players in the development of CSA-AKI. This work provides valuable knowledge for further study of the contribution of gut microbiota in CSA-AKI.

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Triple negative breast cancers (TNBCs) are tumors with a poor treatment response and prognosis. In this study, we propose a new approach, candidate extraction from convolutional neural network (CNN) elements (CECE), for discovery of biomarkers for TNBCs. We used the GSE96058 and GSE81538 datasets to build a CNN model to classify TNBCs and non-TNBCs and used the model to make TNBC predictions for two additional datasets, the cancer genome atlas (TCGA) breast cancer RNA sequencing data and the data from Fudan University Shanghai Cancer Center (FUSCC). Using correctly predicted TNBCs from the GSE96058 and TCGA datasets, we calculated saliency maps for these subjects and extracted the genes that the CNN model used to separate TNBCs from non-TNBCs. Among the TNBC signature patterns that the CNN models learned from the training data, we found a set of 21 genes that can classify TNBCs into two major classes, or CECE subtypes, with distinct overall survival rates (P = 0.0074). We replicated this subtype classification in the FUSCC dataset using the same 21 genes, and the two subtypes had similar differential overall survival rates (P = 0.0490). When all TNBCs were combined from the 3 datasets, the CECE II subtype had a hazard ratio of 1.94 (95% CI, 1.25-3.01; P = 0.0032). The results demonstrate that the spatial patterns learned by the CNN models can be utilized to discover interacting biomarkers otherwise unlikely to be identified by traditional approaches.

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A growing body of evidence suggests that dysbiosis of the human gut microbiota is associated with neurodegenerative diseases like Alzheimer's disease (AD) via neuroinflammatory processes across the microbiota-gut-brain axis. The gut microbiota affects brain health through the secretion of toxins and short-chain fatty acids, which modulates gut permeability and numerous immune functions. Observational studies indicate that AD patients have reduced microbiome diversity, which could contribute to the pathogenesis of the disease. Uncovering the genetic basis of microbial abundance and its effect on AD could suggest lifestyle changes that may reduce an individual's risk for the disease. Using the largest genome-wide association study of gut microbiota genera from the MiBioGen consortium, we used polygenic risk score (PRS) analyses with the "best-fit" model implemented in PRSice-2 and determined the genetic correlation between 119 genera and AD in a discovery sample (ADc12 case/control: 1278/1293). To confirm the results from the discovery sample, we next repeated the PRS analysis in a replication sample (GenADA case/control: 799/778) and then performed a meta-analysis with the PRS results from both samples. Finally, we conducted a linear regression analysis to assess the correlation between the PRSs for the significant genera and the APOE genotypes. In the discovery sample, 20 gut microbiota genera were initially identified as genetically associated with AD case/control status. Of these 20, three genera (Eubacterium fissicatena as a protective factor, Collinsella, and Veillonella as a risk factor) were independently significant in the replication sample. Meta-analysis with discovery and replication samples confirmed that ten genera had a significant correlation with AD, four of which were significantly associated with the APOE rs429358 risk allele in a direction consistent with their protective/risk designation in AD association. Notably, the proinflammatory genus Collinsella, identified as a risk factor for AD, was positively correlated with the APOE rs429358 risk allele in both samples. Overall, the host genetic factors influencing the abundance of ten genera are significantly associated with AD, suggesting that these genera may serve as biomarkers and targets for AD treatment and intervention. Our results highlight that proinflammatory gut microbiota might promote AD development through interaction with APOE. Larger datasets and functional studies are required to understand their causal relationships.

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Investigating functional, temporal, and cell-type expression features of mutations is important for understanding a complex disease. Here, we collected and analyzed common variants and de novo mutations (DNMs) in schizophrenia (SCZ). We collected 2,636 missense and loss-of-function (LoF) DNMs in 2,263 genes across 3,477 SCZ patients (SCZ-DNMs). We curated three gene lists: (a) SCZ-neuroGenes (159 genes), which are intolerant to LoF and missense DNMs and are neurologically important, (b) SCZ-moduleGenes (52 genes), which were derived from network analyses of SCZ-DNMs, and (c) SCZ-commonGenes (120 genes) from a recent GWAS as reference. To compare temporal gene expression, we used the BrainSpan dataset. We defined a fetal effect score (FES) to quantify the involvement of each gene in prenatal brain development. We further employed the specificity indexes (SIs) to evaluate cell-type expression specificity from single-cell expression data in cerebral cortices of humans and mice. Compared with SCZ-commonGenes, SCZ-neuroGenes and SCZ-moduleGenes were highly expressed in the prenatal stage, had higher FESs, and had higher SIs in fetal replicating cells and undifferentiated cell types. Our results suggested that gene expression patterns in specific cell types in early fetal stages might have impacts on the risk of SCZ during adulthood.

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Background: Cardiac surgery-associated acute kidney injury (CSA-AKI) may increase the mortality and incidence rates of chronic kidney disease in critically ill patients. This study aimed to investigate the underlying correlations between urinary proteomic changes and CSA-AKI. Methods: Nontargeted proteomics was performed using nano liquid chromatography coupled with Orbitrap Exploris mass spectrometry (MS) on urinary samples preoperatively and postoperatively collected from patients with CSA-AKI. Gemini C18 silica microspheres were used to separate and enrich trypsin-hydrolysed peptides under basic mobile phase conditions. Differential analysis was conducted to screen out urinary differential expressed proteins (DEPs) among patients with CSA-AKI for bioinformatics. Kyoto Encyclopedia of Genes and Genomes (KEGG) database analysis was adopted to identify the altered signal pathways associated with CSA-AKI. Results: Approximately 2000 urinary proteins were identified and quantified through data-independent acquisition MS, and 324 DEPs associated with AKI were screened by univariate statistics. According to KEGG enrichment analysis, the signal pathway of protein processing in the endoplasmic reticulum was enriched as the most up-regulated DEPs, and cell adhesion molecules were enriched as the most down-regulated DEPs. In protein-protein interaction analysis, the three hub targets in the up-regulated DEPs were α-1-antitrypsin, ß-2-microglobulin and angiotensinogen, and the three key down-regulated DEPs were growth arrest-specific protein 6, matrix metalloproteinase-9 and urokinase-type plasminogen activator. Conclusion: Urinary protein disorder was observed in CSA-AKI due to ischaemia and reperfusion. The application of Gemini C18 silica microspheres can improve the protein identification rate to obtain highly valuable resources for the urinary DEPs of AKI. This work provides valuable knowledge about urinary proteome biomarkers and essential resources for further research on AKI.