Effect of molecular weight of chitosan on quercetin-loaded chitosan nanoparticles.

BACKGROUND: The widespread use of quercetin is limited by its instability, low solubility and poor oral bioavailability. Encapsulation of quercetin using a nanoparticle delivery system is an effective way to overcome these drawbacks. RESULTS: The effect of the molecular weight (Mw) of chitosan (CS) (100, 200, 500 and 1000 kDa) on quercetin-loaded chitosan nanoparticles (QCNPs) was investigated. The structure, stability, release properties and antioxidant activities of the nanoparticles (QCNP-10, QCNP-20, QCNP-50 and QCNP-100) were assessed. Particle size of QCNPs decreased and polydispersity index increased with the increasing Mw of CS. The main forces involved in the formation of QCNPs were hydrogen bonding and hydrophobic interaction. X-ray diffraction verified that quercetin was loaded into CS nanoparticles. The photostability and thermal stability of QCNPs increased with increasing Mw of CS. QCNP-100 exhibited the lowest release rate in a mixture of water and anhydrous ethanol. The antioxidant activities of QCNPs were enhanced with increasing Mw of CS, and QCNP-100 possessed the highest antioxidant activities, which might be relevant to its smallest particle size. CONCLUSION: Overall, these results revealed that the Mw of CS affected the properties of QCNPs, and QCNP-100 possessed the smallest particle, best stability, lowest release rate and highest antioxidant activities. © 2024 Society of Chemical Industry.

Deep learning for multi-type infectious keratitis diagnosis: A nationwide, cross-sectional, multicenter study.

The main cause of corneal blindness worldwide is keratitis, especially the infectious form caused by bacteria, fungi, viruses, and Acanthamoeba. The key to effective management of infectious keratitis hinges on prompt and precise diagnosis. Nevertheless, the current gold standard, such as cultures of corneal scrapings, remains time-consuming and frequently yields false-negative results. Here, using 23,055 slit-lamp images collected from 12 clinical centers nationwide, this study constructed a clinically feasible deep learning system, DeepIK, that could emulate the diagnostic process of a human expert to identify and differentiate bacterial, fungal, viral, amebic, and noninfectious keratitis. DeepIK exhibited remarkable performance in internal, external, and prospective datasets (all areas under the receiver operating characteristic curves > 0.96) and outperformed three other state-of-the-art algorithms (DenseNet121, InceptionResNetV2, and Swin-Transformer). Our study indicates that DeepIK possesses the capability to assist ophthalmologists in accurately and swiftly identifying various infectious keratitis types from slit-lamp images, thereby facilitating timely and targeted treatment.

EGR3 Inhibits Tumor Progression by Inducing Schwann Cell-Like Differentiation.

The mechanism and function of the expression of Schwann characteristics by nevus cells in the mature zone of the dermis are unknown. Early growth response 3 (EGR3) induces Schwann cell-like differentiation of melanoma cells by simulating the process of nevus maturation, which leads to a strong phenotypic transformation of the cells, including the formation of long protrusions and a decrease in cell motility, proliferation, and melanin production. Meanwhile, EGR3 regulates the levels of myelin protein zero (MPZ) and collagen type I alpha 1 chain (COL1A1) through SRY-box transcription factor 10 (SOX10)-dependent and independent mechanisms, by binding to non-strictly conserved motifs, respectively. Schwann cell-like differentiation demonstrates significant benefits in both in vivo and clinical studies. Finally, a CD86-P2A-EGR3 recombinant mRNA vaccine is developed which leads to tumor control through forced cell differentiation and enhanced immune infiltration. Together, these data support further development of the recombinant mRNA as a treatment for cancer.

Comprehensive application of AI algorithms with TCR NGS data for glioma diagnosis.

T-cell receptor (TCR) detection can examine the extent of T-cell immune responses. Therefore, the article analyzed characteristic data of glioma obtained by DNA-based TCR high-throughput sequencing, to predict the disease with fewer biomarkers and higher accuracy. We downloaded data online and obtained six TCR-related diversity indices to establish a multidimensional classification system. By comparing actual presence of the 602 correlated sequences, we obtained two-dimensional and multidimensional datasets. Multiple classification methods were utilized for both datasets with the classification accuracy of multidimensional data slightly less to two-dimensional datasets. This study reduced the TCR ß sequences through feature selection methods like RFECV (Recursive Feature Elimination with Cross-Validation). Consequently, using only the presence of these three sequences, the classification AUC value of 96.67% can be achieved. The combination of the three correlated TCR clones obtained at a source data threshold of 0.1 is: CASSLGGNTEAFF_TRBV12_TRBJ1-1, CASSYSDTGELFF_TRBV6_TRBJ2-2, and CASSLTGNTEAFF_TRBV12_TRBJ1-1. At 0.001, the combination is: CASSLGETQYF_TRBV12_TRBJ2-5, CASSLGGNQPQHF_TRBV12_TRBJ1-5, and CASSLSGNTIYF_TRBV12_TRBJ1-3. This method can serve as a potential diagnostic and therapeutic tool, facilitating diagnosis and treatment of glioma and other cancers.

Artificial sweeteners and risk of incident cardiovascular disease and mortality: evidence from UK Biobank.

BACKGROUND: Artificial sweeteners are widely popular worldwide as substitutes for sugar or caloric sweeteners, but there are still several important unknowns and controversies regarding their associations with cardiovascular disease (CVD). We aimed to extensively assess the association and subgroup variability between artificial sweeteners and CVD and CVD mortality in the UK Biobank cohort, and further investigate the modification effects of genetic susceptibility and the mediation role of type 2 diabetes mellitus (T2DM). METHODS: This study included 133,285 participants in the UK Biobank who were free of CVD and diabetes at recruitment. Artificial sweetener intake was obtained from repeated 24-hour diet recalls. Cox proportional hazard models were used to estimate HRs. Genetic predisposition was estimated using the polygenic risk score (PRS). Furthermore, time-dependent mediation was performed. RESULTS: In our study, artificial sweetener intake (each teaspoon increase) was significantly associated with an increased risk of incident overall CVD (HR1.012, 95%CI: 1.008,1.017), coronary artery disease (CAD) (HR: 1.018, 95%CI: 1.001,1.035), peripheral arterial disease (PAD) (HR: 1.035, 95%CI: 1.010,1.061), and marginally significantly associated with heart failure (HF) risk (HR: 1.018, 95%CI: 0.999,1.038). In stratified analyses, non-whites were at greater risk of incident overall CVD from artificial sweetener. People with no obesity (BMI < 30 kg/m2) also tended to be at greater risk of incident CVD from artificial sweetener, although the obesity interaction is not significant. Meanwhile, the CVD risk associated with artificial sweeteners is independent of genetic susceptibility, and no significant interaction exists between genetic susceptibility and artificial sweeteners in terms of either additive or multiplicative effects. Furthermore, our study revealed that the relationship between artificial sweetener intake and overall CVD is significantly mediated, in large part, by prior T2DM (proportion of indirect effect: 70.0%). In specific CVD subtypes (CAD, PAD, and HF), the proportion of indirect effects ranges from 68.2 to 79.9%. CONCLUSIONS: Our findings suggest significant or marginally significant associations between artificial sweeteners and CVD and its subtypes (CAD, PAD, and HF). The associations are independent of genetic predisposition and are mediated primarily by T2DM. Therefore, the large-scale application of artificial sweeteners should be prudent, and the responses of individuals with different characteristics to artificial sweeteners should be better characterized to guide consumers' artificial sweeteners consumption behavior.

Drip Fertigation Increases Maize Grain Yield by Affecting Phenology, Grain Filling Process, Biomass Accumulation and Translocation: A 4-Year Field Trial.

Drip fertigation (DF) is a widely used technology to increase grain yield with water and fertilizer conservation. However, the mechanism of high grain yield (GY) under DF is still unclear. Here, a four-year field experiment assessed the impacts of four treatments (i.e., conventional irrigation and nitrogen application, CK; drip irrigation with conventional nitrogen fertilization, DI; split-nitrogen fertigation with conventional irrigation, SF; and drip fertigation, DF) on maize phenology, leaf photosynthetic rates, grain filling processes, plant biomass, and GY. The results showed that DF significantly increased maize GY by affecting phenology, grain filling traits, aboveground biomass (BIO) accumulation, and translocation. Specifically, DF significantly increased leaf chlorophyll content, which enhanced leaf photosynthetic rates, and together with an increase of leaf area index, promoted BIO accumulation. As a result, the BIO at the silking stage of DF increased by 29.5%, transported biomass increased by 109.2% (1.2 t ha-1), and the accumulation of BIO after silking increased by 23.1% (1.7 t ha-1) compared with CK. Meanwhile, DF prolonged grain filling days, significantly increased the grain weight of 100 kernels, and promoted GY increase. Compared with CK, the four-year averaged GY and BIO increased by 34.3% and 26.8% under DF; a 29.7%, 46.1%, and 24.2% GY increase and a 30.7%, 39.5%, and 29.9% BIO increase were contributed by irrigation, nitrogen, and coupling effects of irrigation and nitrogen, respectively. These results reveal the high yield mechanism of drip-fertigated maize, and are of important significance for promoting the application of drip fertigation.

Bacteriocins in Cancer Treatment: Mechanisms and Clinical Potentials.

Cancer poses a severe threat to human health. Although conventional chemotherapy remains a cornerstone of cancer treatment, its significant side effects and the growing issue of drug resistance necessitate the urgent search for more efficient and less toxic anticancer drugs. In recent years, bacteriocins, antimicrobial peptides of microbial origin, have garnered significant attention due to their targeted antitumor activity. This unique activity is mainly attributed to their cationic and amphiphilic nature, which enables bacteriocins to specifically kill tumor cells without harming normal cells. When involving non-membrane-disrupting mechanisms, such as apoptosis induction, cell cycle blockade, and metastasis inhibition, the core mechanism of action is achieved by disrupting cell membranes, which endows bacteriocins with low drug resistance and high selectivity. However, the susceptibility of bacteriocins to hydrolysis and hemolysis in vivo limits their clinical application. To overcome these challenges, structural optimization of bacteriocins or their combination with nanotechnology is proposed for future development. This review aims to study the mechanism of action and current research status of bacteriocins as anticancer treatments, thus providing new insights for their clinical development and application.

Associations between leisure-time physical activity and the prevalence and incidence of osteoporosis disease: Cross-sectional and prospective findings from the UK biobank.

BACKGROUND: Previous studies underscore the protective role of physical activity (PA) in bone health, yet the relationship between different PA categories and osteoporosis risk remains less explored. Understanding the relationships helps tailor health recommendations and policies to maximize the effects of preventing osteoporosis. METHODS: The cross-sectional study involves 488,403 UK Biobank participants with heel quantitative ultrasound-estimated bone mineral density (eBMD) data. The longitudinal cohort involves 471,394 UK Biobank participants without initial osteoporosis and with follow-up records. PA exposure categories in our study included sedentary behavior (SB), total PA (TPA), and different category-specific PA including household, leisure, and work PA. The cases of osteoporosis were assessed using the International Classification of Diseases, 10th revision (ICD-10). The linear, logistic, and Cox proportional hazard regression models were used in our study. RESULTS: In the cross-sectional study, 15,818 (3.28 %) participants had osteoporosis. TPA levels have a positive correlation with eBMD and a negative correlation with osteoporosis prevalence. Among different categories of PA, higher levels of leisure PA were correlated with increased eBMD and a lower osteoporosis risk (leisure PA: OR: 0.83, 95 % CI: 0.79 to 0.86;). In the longitudinal study, 16,058 (17.6 % male, 82.4 % female) (3.41 %) individuals developed osteoporosis during an average follow-up of 13 years. We observed consistent protective effects of high levels of PA on osteoporosis incidence risk, particularly within the category of leisure PA (TPA: HR: 0.78, 95 % CI: 0.74 to 0.82; leisure PA:HR: 0.83, 95 % CI: 0.80 to 0.87). Such associations are independent of genetic predisposition, with no evidence of gene-PA interactions, and keep steady among individuals using drugs affecting bone-density. Moreover, among different leisure PA items, strenuous sports, other exercises, and walking for pleasure conferred a substantial protective effect against osteoporosis. Additionally, non-elderly individuals and males exhibited lower osteoporosis risk from PA. CONCLUSION: This study highlights activity categories differently associated with the risk of osteoporosis. Adherence to frequent leisure PA may have a protective effect against osteoporosis. Such associations are independent of genetic susceptibility to osteoporosis and keep steady among individuals using drugs affecting bone-density. This highlights that leisure PA could be suggested as a more effective intervention in the primary prevention of osteoporosis.

A Novel Noise Reduction Approach of Acoustic Emission (AE) Signals in the SiC Lapping Process on Fixed Abrasive Pads.

Acoustic emission (AE) technology has been widely utilized to monitor the SiC wafer lapping process. The root-mean-square (RMS) of the time-domain eigenvalues of the AE signal has a linear relationship with the material removal rate (MRR). However, the existence of background noise severely reduces signal monitoring accuracy. Noise interference often leads to increased RMS deviation and signal distortion. In the study presented in this manuscript, a frequency threshold noise reduction approach was developed by combining and improving wavelet packet noise reduction and spectral subtraction noise reduction techniques. Three groups of SiC lapping experiments were conducted on a fixed abrasive pad, and the lapping acoustic signals were processed using three different noise reduction approaches: frequency threshold, wavelet packet, and spectral subtraction. The results show that the noise reduction method using the frequency threshold is the most effective, with the best coefficient of determination (R2) for the linear fit of the RMS to the MRR.

Alcoholic Liver Disease in China: A Disease Influenced by Complex Social Factors That Should Not Be Neglected.

Alcoholic liver disease (ALD) encompasses liver damage caused by chronic, excessive alcohol consumption. It manifests initially as marked hepatocellular steatosis and can progress to steatohepatitis, liver fibrosis, and cirrhosis. With China's rapid economic growth, coupled with a complex social background and the influence of a deleterious wine culture, the number of patients with ALD in China has increased significantly; the disease has become a social and health problem that cannot be ignored. In this review, we briefly described the social factors affecting ALD in China and elaborated on differences between alcoholic and other liver diseases in terms of complications (e.g., cirrhosis, upper gastrointestinal bleeding, hepatic encephalopathy, hepatocellular carcinoma, addiction, and other extrahepatic diseases). We also emphasized that ALD was more dangerous and difficult to treat than other liver diseases due to its complications, and that precise and effective treatment measures were lacking. In addition, we considered new ideas and treatment methods that may be generated in the future.

Ink-based additive manufacturing for electrochemical applications.

Additive manufacturing (AM), commonly known as three-dimensional (3D) printing, has drawn substantial attention in recent decades due to its efficiency and precise control in part fabrication. The limitations of conventional fabrication processes, especially regarding geometry complexity, supply chain, and environmental impact, have prompted the exploration of diverse AM technologies in electrochemistry. Especially, three ink-based AM techniques, binder jet printing (BJP), direct ink writing (DIW), and Inkjet Printing (IJP), have been extensively applied by numerous research teams to produce electrodes, catalyst scaffolds, supercapacitors, batteries, etc. BJP's versatility in utilizing a wide range of materials as powder feedstock promotes its potential for various electrode and battery applications. DIW and IJP stand out for their ability to handle multi-material manufacturing tasks and deliver high printing resolution. To capture recent advancements in this field, we present a comprehensive review of the applications of BJP, DIW, and IJP techniques in fabricating electrochemical devices and components. This review intends to provide an overview of the process-structure-property relationship in electrochemical materials and components across diverse applications manufactured using AM techniques. We delve into how the significantly improved design freedom over the structure offered by these ink-based AM techniques highlights the performance of electrochemical products. Moreover, we highlight their advantages in terms of material compatibility, geometry control, and cost-effectiveness. In specific cases, we also compare the performance of electrochemical components fabricated using AM and conventional manufacturing methods. Finally, we conclude this review article by offering some insights into the future development in this research field.

RHOF activation of AKT/ß-catenin signaling pathway drives acute myeloid leukemia progression and chemotherapy resistance.

Acute myeloid leukemia (AML) is a clonal malignancy originating from leukemia stem cells, characterized by a poor prognosis, underscoring the necessity for novel therapeutic targets and treatment methodologies. This study focuses on Ras homolog family member F, filopodia associated (RHOF), a Rho guanosine triphosphatase (GTPase) family member. We found that RHOF is overexpressed in AML, correlating with an adverse prognosis. Our gain- and loss-of-function experiments revealed that RHOF overexpression enhances proliferation and impedes apoptosis in AML cells in vitro. Conversely, genetic suppression of RHOF markedly reduced the leukemia burden in a human AML xenograft mouse model. Furthermore, we investigated the synergistic effect of RHOF downregulation and chemotherapy, demonstrating significant therapeutic efficacy in vivo. Mechanistically, RHOF activates the AKT/ß-catenin signaling pathway, thereby accelerating the progression of AML. Our findings elucidate the pivotal role of RHOF in AML pathogenesis and propose RHOF inhibition as a promising therapeutic approach for AML management.

Establishment of a semi-continuous scale-down clone screening model for intensified perfusion culture.

PURPOSE: Perfusion cultures have been extensively used in the biotechnology industry to achieve high yields of recombinant products, especially those with stability issue. The WuXiUP™ platform represents a novel intensified perfusion that can achieve ultra-high productivity. This study describes a representative scale-down 24-deep well plate (24-DWP) cell culture model for intensified perfusion clone screening. METHODS: Clonal cell lines were expanded and evaluated in 24-DWP semi-continuous culture. Cell were sampled and counted daily with the aid of an automated liquid handler and high-throughput cell counter. To mimic perfusion culture, 24-DWP plates were spun down and resuspended with fresh medium daily. Top clones were ranked based on growth profiles and productivities. The best performing clones were evaluated on bioreactors. RESULTS: The selected clones achieved volumetric productivity (Pv) up to 5 g/L/day when expressing a monoclonal antibody, with the accumulative harvest Pv exceeding 60 g/L in a 21-day cell culture. Product quality attributes of clones cultured in 24-DWP were comparable with those from bioreactors. A high seeding strategy further shortened the clone screening timeline. CONCLUSION: In this study, a 24-DWP semi-continuous scale-down model was successfully developed to screen for cell lines suitable for intensified perfusion culture.

Ultraviolet-Visible-near infrared induced photocatalytic H2 evolution over S-scheme Cu2-xSe/ZnSe heterojunction with surface plasma effects.

Localized surface plasmon resonance (LSPR) effect plays a crucial role in the field of solar energy utilization. In this work, we successfully prepared a Cu2-xSe/ZnSe S-scheme heterojunction with a broad-spectrum response using the hot-injection and low-temperature water bath method. Importantly, we demonstrated that the photothermal effect induced by the LSPR of nonstoichiometric Cu2-xSe can significantly improve the slow kinetics of water splitting, resulting in an apparent activation energy reduction from 50.1 to 28.7 kJ·mol-1. This improvement is responsible for achieving the highest photocatalytic H2 evolution rate of 63.6 mmol·g-1·h-1 over 2.7 % Cu2-xSe/ZnSe under the wavelength ranged from 200 to 2500 nm, which is 3.4 and 5.6 times higher than that of ZnSe and Cu2-xSe, respectively. Furthermore, the composite exhibits a remarkable H2 production rate of 0.108 mmol·g-1·h-1 under near-infrared spectroscopy (800<λ<2500 nm), while ZnSe shows limited capability in H2 releasing. Additionally, Cu2-xSe/ZnSe demonstrates distinct photocurrent response when λ > 800 nm. The enhanced performance in H2 evolution can be attributed to the synergistic effect of LSPR-induced light absorption and S-scheme heterojunction, which not only expands the light absorption range to the near-infrared region but also facilitates hot electron injection, charge carrier separation and transfer, leading to a faster surface reaction kinetics. This study provides an effective approach for designing a broad-spectrum light responsive non-precious metal-based photothermal-assisted photocatalytic system.

Recent advances in biomimetic strategies for the immunotherapy of glioblastoma.

Immunotherapy is regarded as one of the most promising approaches for treating tumors, with a multitude of immunotherapeutic thoughts currently under consideration for the lethal glioblastoma (GBM). However, issues with immunotherapeutic agents, such as limited in vivo stability, poor blood-brain barrier (BBB) penetration, insufficient GBM targeting, and represented monotherapy, have hindered the success of immunotherapeutic interventions. Moreover, even with the aid of conventional drug delivery systems, outcomes remain suboptimal. Biomimetic strategies seek to overcome these formidable drug delivery challenges by emulating nature's intelligent structures and functions. Leveraging the variety of biological structures and functions, biomimetic drug delivery systems afford a versatile platform with enhanced biocompatibility for the co-delivery of diverse immunotherapeutic agents. Moreover, their inherent capacity to traverse the BBB and home in on GBM holds promise for augmenting the efficacy of GBM immunotherapy. Thus, this review begins by revisiting the various thoughts and agents on immunotherapy for GBM. Then, the barriers to successful GBM immunotherapy are analyzed, and the corresponding biomimetic strategies are explored from the perspective of function and structure. Finally, the clinical translation's current state and prospects of biomimetic strategy are addressed. This review aspires to provide fresh perspectives on the advancement of immunotherapy for GBM.

A cross-omics data analysis strategy for metabolite-microbe pair identification.

Given the pivotal roles of metabolomics and microbiomics, numerous data mining approaches aim to uncover their intricate connections. However, the complex many-to-many associations between metabolome-microbiome profiles yield numerous statistically significant but biologically unvalidated candidates. To address these challenges, we introduce BiOFI, a strategic framework for identifying metabolome-microbiome correlation pairs (Bi-Omics). BiOFI employs a comprehensive scoring system, incorporating intergroup differences, effects on feature correlation networks, and organism abundance. Meanwhile, it establishes a built-in database of metabolite-microbe-KEGG functional pathway linking relationships. Furthermore, BiOFI can rank related feature pairs by combining importance scores and correlation strength. Validation on a dataset of cesarean-section infants confirms the strategy's validity and interpretability. The BiOFI R package is freely accessible at https://github.com/chentianlu/BiOFI.

Dynamic evolution of COVID-19 vaccine hesitancy over 2021-2023 among Chinese population: Repeated nationwide cross-sectional study.

Globally, the rollout of COVID-19 vaccine had been faced with a significant barrier in the form of vaccine hesitancy. This study adopts a multi-stage perspective to explore the prevalence and determinants of COVID-19 vaccine hesitancy, focusing on their dynamic evolutionary features. Guided by the integrated framework of the 3Cs model (complacency, confidence, and convenience) and the EAH model (environmental, agent, and host), this study conducted three repeated national cross-sectional surveys. These surveys carried out from July 2021 to February 2023 across mainland China, targeted individuals aged 18 and older. They were strategically timed to coincide with three critical vaccination phases: universal coverage (stage 1), partial coverage (stage 2), and key population coverage (stage 3). From 2021 to 2023, the surveys examined sample sizes of 29 925, 6659, and 5407, respectively. The COVID-19 vaccine hesitation rates increased from 8.39% in 2021 to 29.72% in 2023. Urban residency, chronic condition, and low trust in vaccine developer contributed to significant COVID-19 vaccine hesitancy across the pandemic. Negative correlations between the intensity of vaccination policies and vaccine hesitancy, and positive correlations between vaccine hesitancy and long COVID, were confirmed. This study provides insights for designing future effective vaccination programs for emerging vaccine-preventable infectious X diseases.

A novel real-time model for predicting acute kidney injury in critically ill patients within 12 hours.

BACKGROUND: A major challenge in prevention and early treatment of acute kidney injury (AKI) is the lack of high-performance predictors in critically ill patients. Therefore, we innovatively constructed U-AKIpredTM for predicting AKI in critically ill patients within 12 h of panel measurement. METHODS: The prospective cohort study included 680 patients in the training set and 249 patients in the validation set. After performing inclusion and exclusion criteria, 417 patients were enrolled in the training set and 164 patients were enrolled in the validation set finally. AKI was diagnosed by Kidney Disease Improving Global Outcomes (KDIGO) criteria. RESULTS: Twelve urinary kidney injury biomarkers (mALB, IgG, TRF, α1MG, NAG, NGAL, KIM-1, L-FABP, TIMP2, IGFBP7, CAF22 and IL-18) exhibited good predictive performance for AKI within 12 h in critically ill patients. U-AKIpredTM, combined with three crucial biomarkers (α1MG, L-FABP and IGFBP7) by multivariate logistic regression analysis, exhibited better predictive performance for AKI in critically ill patients within 12 h than the other twelve kidney injury biomarkers. The area under the curve (AUC) of the U-AKIpredTM, as a predictor of AKI within 12 h, was 0.802 (95% CI: 0.771-0.833, P < 0.001) in the training set and 0.844 (95% CI: 0.792-0.896, P < 0.001) in validation cohort. A nomogram based on the results of the training and validation sets of U-AKIpredTM was developed which showed optimal predictive performance for AKI. The fitting effect and prediction accuracy of U-AKIpredTM was evaluated by multiple statistical indicators. To provide a more flexible predictive tool, the dynamic nomogram (https://www.xsmartanalysis.com/model/U-AKIpredTM) was constructed using a web-calculator. Decision curve analysis (DCA) and a clinical impact curve were used to reveal that U-AKIpredTM with the three crucial biomarkers had a higher net benefit than these twelve kidney injury biomarkers respectively. The net reclassification index (NRI) and integrated discrimination index (IDI) were used to improve the significant risk reclassification of AKI compared with the 12 kidney injury biomarkers. The predictive efficiency of U-AKIpredTM was better than the NephroCheck® when testing for AKI and severe AKI. CONCLUSION: U-AKIpredTM is an excellent predictive model of AKI in critically ill patients within 12 h and would assist clinicians in identifying those at high risk of AKI.