GNNGL-PPI: multi-category prediction of protein-protein interactions using graph neural networks based on global graphs and local subgraphs.

Most proteins exert their functions by interacting with other proteins, making the identification of protein-protein interactions (PPI) crucial for understanding biological activities, pathological mechanisms, and clinical therapies. Developing effective and reliable computational methods for predicting PPI can significantly reduce the time-consuming and labor-intensive associated traditional biological experiments. However, accurately identifying the specific categories of protein-protein interactions and improving the prediction accuracy of the computational methods remain dual challenges. To tackle these challenges, we proposed a novel graph neural network method called GNNGL-PPI for multi-category prediction of PPI based on global graphs and local subgraphs. GNNGL-PPI consisted of two main components: using Graph Isomorphism Network (GIN) to extract global graph features from PPI network graph, and employing GIN As Kernel (GIN-AK) to extract local subgraph features from the subgraphs of protein vertices. Additionally, considering the imbalanced distribution of samples in each category within the benchmark datasets, we introduced an Asymmetric Loss (ASL) function to further enhance the predictive performance of the method. Through evaluations on six benchmark test sets formed by three different dataset partitioning algorithms (Random, BFS, DFS), GNNGL-PPI outperformed the state-of-the-art multi-category prediction methods of PPI, as measured by the comprehensive performance evaluation metric F1-measure. Furthermore, interpretability analysis confirmed the effectiveness of GNNGL-PPI as a reliable multi-category prediction method for predicting protein-protein interactions.

Pathogenesis and virulence of Heartland virus.

Heartland virus (HRTV), an emerging tick-borne pathogenic bunyavirus, has been a concern since 2012, with an increasing incidence, expanding geographical distribution, and high pathogenicity in the United States. Infection from HRTV results in fever, thrombocytopenia, and leucopenia in humans, and in some cases, symptoms can progress to severe outcomes, including haemorrhagic disease, multi-organ failure, and even death. Currently, no vaccines or antiviral drugs are available for treatment of the HRTV disease. Moreover, little is known about HRTV-host interactions, viral replication mechanisms, pathogenesis and virulence, further hampering the development of vaccines and antiviral interventions. Here, we aimed to provide a brief review of HRTV epidemiology, molecular biology, pathogenesis and virulence on the basis of published article data to better understand this virus and provide clues for further study.

Membrane-Active All-Hydrocarbon-Stapled α-Helical Amphiphilic Tat Peptides: Broad-Spectrum Antibacterial Activity and Low Incidence of Drug Resistance.

Multidrug resistance against conventional antibiotics has dramatically increased the difficulty of treatment and accelerated the need for novel antibacterial agents. The peptide Tat (47-57) is derived from the transactivating transcriptional activator of human immunodeficiency virus 1, which is well-known as a cell-penetrating peptide in mammalian cells. However, it is also reported that the Tat peptide (47-57) has antifungal activity. In this study, a series of membrane-active hydrocarbon-stapled α-helical amphiphilic peptides were synthesized and evaluated as antibacterial agents against Gram-positive and Gram-negative bacteria, including multidrug-resistant strains. The impact of hydrocarbon staple, the position of aromatic amino acid residue in the hydrophobic face, the various types of aromatic amino acids, and the hydrophobicity on bioactivity were also investigated and discussed in this study. Among those synthesized peptides, analogues P3 and P10 bearing a l-2-naphthylalanine (Φ) residue at the first position and a Tyr residue at the eighth position demonstrated the highest antimicrobial activity and negligible hemolytic toxicity. Notably, P3 and P10 showed obviously enhanced antimicrobial activity against multidrug-resistant bacteria, low drug resistance, high cell selectivity, extended half-life in plasma, and excellent performance against biofilm. The antibacterial mechanisms of P3 and P10 were also preliminarily investigated in this effort. In conclusion, P3 and P10 are promising antimicrobial alternatives for the treatment of the antimicrobial-resistance crisis.

Characterising global antimicrobial resistance research explains why One Health solutions are slow in development: An application of AI-based gap analysis.

The global health crisis posed by increasing antimicrobial resistance (AMR) implicitly requires solutions based a One Health approach, yet multisectoral, multidisciplinary research on AMR is rare and huge knowledge gaps exist to guide integrated action. This is partly because a comprehensive survey of past research activity has never performed due to the massive scale and diversity of published information. Here we compiled 254,738 articles on AMR using Artificial Intelligence (AI; i.e., Natural Language Processing, NLP) methods to create a database and information retrieval system for knowledge extraction on research perfomed over the last 20 years. Global maps were created that describe regional, methodological, and sectoral AMR research activities that confirm limited intersectoral research has been performed, which is key to guiding science-informed policy solutions to AMR, especially in low-income countries (LICs). Further, we show greater harmonisation in research methods across sectors and regions is urgently needed. For example, differences in analytical methods used among sectors in AMR research, such as employing culture-based versus genomic methods, results in poor communication between sectors and partially explains why One Health-based solutions are not ensuing. Therefore, our analysis suggest that performing culture-based and genomic AMR analysis in tandem in all sectors is crucial for data integration and holistic One Health solutions. Finally, increased investment in capacity development in LICs should be prioritised as they are places where the AMR burden is often greatest. Our open-access database and AI methodology can be used to further develop, disseminate, and create new tools and practices for AMR knowledge and information sharing.

Synergistic Effects of TiO2 and Carbon Black for Water Evaporation-Induced Electricity Generation.

Water evaporation-induced electricity generators (WEGs) have drawn widespread attention in the field of hydrovoltaic technology, which can convert atmospheric thermal energy into sustainable electric power. However, it is restricted in the wide application of WEGs due to the low power output, complex fabrication process, and high cost. Herein, we present a simple and effective approach to fabricate TiO2-carbon black film-based WEGs (TC-WEGs). A single TC-WEG device can sustainably output an open-circuit voltage of 1.9 V and a maximum power density of 40.9 µW/cm2. Moreover, it has been shown that TC-WEGs exhibit stable electrical energy output when operating in seawater, which can yield a short-circuit current of 1.2 µA. The superior electricity generation performance can be attributed to the intrinsic characteristics of the TC-WEGs, including hydrophilicity, porous structure, and electrical conductivity. This work provides an important reference for the constant harvesting of clean energy.

Calreticulin regulates hepatic stellate cell activation through modulating TGF-beta-induced Smad signaling.

Liver fibrosis is characterized by excessive deposition of extracellular matrix (ECM) as a wound healing process. Activated hepatic stellate cells (HpSCs) are the major producer of the ECM and play a central role in liver fibrogenesis. It has been widely accepted that elimination of activated HpSCs or reversion to a quiescent state can be a feasible strategy for resolving the disease, further highlighting the urgent need for novel therapeutic targets. Calreticulin (CRT) is a molecular chaperone that normally resides in the endoplasmic reticulum (ER), important in protein folding and trafficking through the secretory pathway. CRT also plays a critical role in calcium (Ca2+) homeostasis, with its Ca2+ storage capacity. In the current study, we aimed to demonstrate its function in directing HpSC activation. In a mouse liver injury model, CRT was up-regulated in HpSCs. In cellular experiments, we further showed that this activation was through modulating the canonical TGF-ß signaling. As down-regulation of CRT in HpSCs elevated intracellular Ca2+ levels through a form of Ca2+ influx, named store-operated Ca2+ entry (SOCE), we examined whether moderating SOCE affected TGF-ß signaling. Interestingly, blocking SOCE had little effect on TGF-ß-induced gene expression. In contrast, inhibition of ER Ca2+ release using the inositol trisphosphate receptor inhibitor 2-APB increased TGF-ß signaling. Treatment with 2-APB did not alter SOCE but decreased intracellular Ca2+ at the basal level. Indeed, adjusting Ca2+ concentrations by EGTA or BAPTA-AM chelation further enhanced TGF-ß-induced signaling. Our results suggest a crucial role of CRT in the liver fibrogenic process through modulating Ca2+ concentrations and TGF-ß signaling in HpSCs, which may provide new information and help advance the current discoveries for liver fibrosis.

Lipid nephrotoxicity mediated by HIF-1α activation accelerates tubular injury in diabetic nephropathy.

This study is intended to explore the effect of hypoxia-inducible factor-1α (HIF-1α) activation on lipid accumulation in the diabetic kidney. A type 1 diabetic rat model was established by STZ intraperitoneal injection. Cobalt chloride (CoCl2) and YC-1 were used as the HIF-1α activator and antagonist, respectively. CoCl2 treatment significantly increased HIF-1α expression, accelerated lipid deposition, and accelerated tubular injury in diabetic kidneys. In vitro, CoCl2 effectively stabilized HIF-1α and increased its transportation from the cytoplasm to the nucleus, which was accompanied by significantly increased lipid accumulation in HK-2 cells. Furthermore, results obtained in vivo showed that HIF-1α protein expression in the renal tubules of diabetic rats was significantly downregulated by YC-1 treatment. Meanwhile, lipid accumulation in the tubules of the DM + YC-1 group was markedly decreased in comparison to the DM + DMSO group. Accordingly, PAS staining revealed that the pathological injury caused to the tubular epithelial cells was alleviated by YC-1 treatment. Furthermore, the blood glucose level, urine albumin creatinine ratio, and NAG creatinine ratio in the DM + YC-1 group were significantly decreased compared to the DM + DMSO group. Moreover, the protein expression levels of transforming growth factor ß1 (TGF-ß1) and connective tissue growth factor (CTGF) in diabetic kidneys were decreased by YC-1 treatment. Our findings demonstrate that the activation of HIF-1α contributed to interstitial injury in a rat model of diabetic nephropathy and that the underlying mechanism involved the induction of lipid accumulation.

Hippocampal transcriptomic analyses reveal the potential antiapoptotic mechanism of a novel anticonvulsant agent Q808 on pentylenetetrazol-induced epilepsy in rats.

Brain apoptosis is one of the main causes of epileptogenesis. The antiapoptotic effect and potential mechanism of Q808, an innovative anticonvulsant chemical, have never been reported. In this study, the seizure stage and latency to reach stage 2 of pentylenetetrazol (PTZ) seizure rat model treated with Q808 were investigated. The morphological change and neuronal apoptosis in the hippocampus were detected by hematoxylin and eosin (HE) and terminal deoxynucleotidyl transferase-mediated dUTP nick end-labeling (TUNEL) staining, respectively. The hippocampal transcriptomic changes were observed using RNA sequencing (RNA-seq). The expression levels of hub genes were verified by quantitative reverse-transcription PCR (qRT-PCR). Results revealed that Q808 could allay the seizure score and prolong the stage 2 latency in seizure rats. The morphological changes of neurons and the number of apoptotic cells in the DG area were diminished by Q808 treatment. RNA-seq analysis revealed eight hub genes, including Map2k3, Nfs1, Chchd4, Hdac6, Siglec5, Slc35d3, Entpd1, and LOC103690108, and nine hub pathways among the control, PTZ, and Q808 groups. Hub gene Nfs1 was involved in the hub pathway sulfur relay system, and Map2k3 was involved in the eight remaining hub pathways, including Amyotrophic lateral sclerosis, Cellular senescence, Fc epsilon RI signaling pathway, GnRH signaling pathway, Influenza A, Rap1 signaling pathway, TNF signaling pathway, and Toll-like receptor signaling pathway. qRT-PCR confirmed that the mRNA levels of these hub genes were consistent with the RNA-seq results. Our findings might contribute to further studies exploring the new apoptosis mechanism and actions of Q808.

The PAX LC Trial: A Decentralized, Phase 2, Randomized, Double-blind Study of Nirmatrelvir/Ritonavir Compared with Placebo/Ritonavir for Long COVID.

INTRODUCTION: Individuals with long COVID lack evidence-based treatments and have difficulty participating in traditional site-based trials. Our digital, decentralized trial investigates the efficacy and safety of nirmatrelvir/ritonavir, targeting viral persistence as a potential cause of long COVID. METHODS: The PAX LC trial (NCT05668091) is a Phase 2, 1:1 randomized, double-blind, superiority, placebo-controlled trial in 100 community-dwelling, highly symptomatic adult participants with long COVID residing in the 48 contiguous US states to determine the efficacy, safety, and tolerability of 15 days of nirmatrelvir/ritonavir compared with placebo/ritonavir. Participants are recruited via patient groups, cultural ambassadors, and social media platforms. Medical records are reviewed through a platform facilitating participant-mediated data acquisition from electronic health records nationwide. During the drug treatment, participants complete daily digital diaries using a web-based application. Blood draws for eligibility and safety assessments are conducted at or near participants' homes. The study drug is shipped directly to participants' homes. The primary endpoint is the PROMIS-29 Physical Health Summary Score difference between baseline and Day 28, evaluated by a mixed model repeated measure analysis. Secondary endpoints include PROMIS-29 (Mental Health Summary Score and all items), Modified GSQ-30 with supplemental symptoms questionnaire, COVID Core Outcome Measures for Recovery, EQ-5D-5L (Utility Score and all items), PGIS 1 and 2, PGIC 1 and 2, and healthcare utilization. The trial incorporates immunophenotyping to identify long COVID biomarkers and treatment responders. CONCLUSION: The PAX LC trial uses a novel decentralized design and a participant-centric approach to test a 15-day regimen of nirmatrelvir/ritonavir for long COVID.

JMJD6 Autoantibodies as a Potential Biomarker for Inflammation-Related Diseases.

Inflammation is closely associated with cerebrovascular diseases, cardiovascular diseases, diabetes, and cancers, and it is accompanied by the development of autoantibodies in the early stage of inflammation-related diseases. Hence, it is meaningful to discover novel antibody biomarkers targeting inflammation-related diseases. In this study, Jumonji C-domain-containing 6 (JMJD6) was identified by the serological identification of antigens through recombinant cDNA expression cloning. In particular, JMJD6 is an antigen recognized in serum IgG from patients with unstable angina pectoris (a cardiovascular disease). Then, the serum antibody levels were examined using an amplified luminescent proximity homogeneous assay-linked immunosorbent assay and a purified recombinant JMJD6 protein as an antigen. We observed elevated levels of serum anti-JMJD6 antibodies (s-JMJD6-Abs) in patients with inflammation-related diseases such as ischemic stroke, acute myocardial infarction (AMI), diabetes mellitus (DM), and cancers (including esophageal cancer, EC; gastric cancer; lung cancer; and mammary cancer), compared with the levels in healthy donors. The s-JMJD6-Ab levels were closely associated with some inflammation indicators, such as C-reactive protein and intima-media thickness (an atherosclerosis index). A better postoperative survival status of patients with EC was observed in the JMJD6-Ab-positive group than in the negative group. An immunohistochemical analysis showed that JMJD6 was highly expressed in the inflamed mucosa of esophageal tissues, esophageal carcinoma tissues, and atherosclerotic plaques. Hence, JMJD6 autoantibodies may reflect inflammation, thereby serving as a potential biomarker for diagnosing specific inflammation-related diseases, including stroke, AMI, DM, and cancers, and for prediction of the prognosis in patients with EC.

Bimetal-Organic Frameworks Incorporating Both Hard and Soft Base Active Sites for Heavy Metal Ion Capture.

The design and synthesis of stable porous materials capable of removing both hard and soft metal ions pose a significant challenge. In this study, a novel metal-organic framework (MOF) adsorbent named CdK-m-COTTTB was developed. This MOF material was constructed using sulfur-rich m-cyclooctatetrathiophene-tetrabenzoate (m-H4COTTTB) as the organic ligand and oxygen-rich bimetallic clusters as the inorganic nodes. The incorporation of both soft and hard base units within the MOF structure enables effective removal of various heavy metal ions, including both soft and hard acid species. In single-component experiments, the adsorption capacity of CdK-m-COTTTB for Pb2+, Tb3+, and Zr4+ ions reached levels of 636.94, 432.90, and 357.14 mg·g-1, respectively, which is comparable to specific MOF absorbents. The rapid adsorption process was found to be chemisorption. Furthermore, CdK-m-COTTTB exhibited the capability to remove at least 12 different metal ions in both separate and multicomponent solutions. The material demonstrated excellent acid-base stability and renewability, which are advantageous for practical applications. CdK-m-COTTTB represents the first reported pristine MOF material for the removal of both hard and soft acid metal ions. This work serves as inspiration for the design and synthesis of porous crystalline materials that can efficiently remove diverse heavy metal pollutants.

A comprehensive review of the recent advances on predicting drug-target affinity based on deep learning.

Accurate calculation of drug-target affinity (DTA) is crucial for various applications in the pharmaceutical industry, including drug screening, design, and repurposing. However, traditional machine learning methods for calculating DTA often lack accuracy, posing a significant challenge in accurately predicting DTA. Fortunately, deep learning has emerged as a promising approach in computational biology, leading to the development of various deep learning-based methods for DTA prediction. To support researchers in developing novel and highly precision methods, we have provided a comprehensive review of recent advances in predicting DTA using deep learning. We firstly conducted a statistical analysis of commonly used public datasets, providing essential information and introducing the used fields of these datasets. We further explored the common representations of sequences and structures of drugs and targets. These analyses served as the foundation for constructing DTA prediction methods based on deep learning. Next, we focused on explaining how deep learning models, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformer, and Graph Neural Networks (GNNs), were effectively employed in specific DTA prediction methods. We highlighted the unique advantages and applications of these models in the context of DTA prediction. Finally, we conducted a performance analysis of multiple state-of-the-art methods for predicting DTA based on deep learning. The comprehensive review aimed to help researchers understand the shortcomings and advantages of existing methods, and further develop high-precision DTA prediction tool to promote the development of drug discovery.

Hypertension Trends and Disparities Over 12 Years in a Large Health System: Leveraging the Electronic Health Records.

BACKGROUND: The digital transformation of medical data enables health systems to leverage real-world data from electronic health records to gain actionable insights for improving hypertension care. METHODS AND RESULTS: We performed a serial cross-sectional analysis of outpatients of a large regional health system from 2010 to 2021. Hypertension was defined by systolic blood pressure ≥140 mm Hg, diastolic blood pressure ≥90 mm Hg, or recorded treatment with antihypertension medications. We evaluated 4 methods of using blood pressure measurements in the electronic health record to define hypertension. The primary outcomes were age-adjusted prevalence rates and age-adjusted control rates. Hypertension prevalence varied depending on the definition used, ranging from 36.5% to 50.9% initially and increasing over time by ≈5%, regardless of the definition used. Control rates ranged from 61.2% to 71.3% initially, increased during 2018 to 2019, and decreased during 2020 to 2021. The proportion of patients with a hypertension diagnosis ranged from 45.5% to 60.2% initially and improved during the study period. Non-Hispanic Black patients represented 25% of our regional population and consistently had higher prevalence rates, higher mean systolic and diastolic blood pressure, and lower control rates compared with other racial and ethnic groups. CONCLUSIONS: In a large regional health system, we leveraged the electronic health record to provide real-world insights. The findings largely reflected national trends but showed distinctive regional demographics and findings, with prevalence increasing, one-quarter of the patients not controlled, and marked disparities. This approach could be emulated by regional health systems seeking to improve hypertension care.

Development of a Decafluorobiphenyl Cyclized Peptide Targeting the NEMO-IKKα/ß Interaction that Enhances Cell Penetration and Attenuates Lipopolysaccharide-Induced Acute Lung Injury.

Aberrant canonical NF-κB signaling has been implicated in diseases, such as autoimmune disorders and cancer. Direct disruption of the interaction of NEMO and IKKα/ß has been developed as a novel way to inhibit the overactivation of NF-κB. Peptides are a potential solution for disrupting protein-protein interactions (PPIs); however, they typically suffer from poor stability in vivo and limited tissue penetration permeability, hampering their widespread use as new chemical biology tools and potential therapeutics. In this work, decafluorobiphenyl-cysteine SNAr chemistry, molecular modeling, and biological validation allowed the development of peptide PPI inhibitors. The resulting cyclic peptide specifically inhibited canonical NF-κB signaling in vitro and in vivo, and presented positive metabolic stability, anti-inflammatory effects, and low cytotoxicity. Importantly, our results also revealed that cyclic peptides had huge potential in acute lung injury (ALI) treatment, and confirmed the role of the decafluorobiphenyl-based cyclization strategy in enhancing the biological activity of peptide NEMO-IKKα/ß inhibitors. Moreover, it provided a promising method for the development of peptide-PPI inhibitors.

A Content Analysis of Online Messages about Sugar-Sweetened Beverages.

Media campaigns can reduce or promote the consumption of sugar-sweetened beverages (SSBs). Brief, US-based English-language online messages were gathered from searchable media platforms, a process that yielded 112 anti-SSB videos and 29 pro-SSB commercials. Using a combination of inductive and deductive methods, a content analysis of those messages was conducted to identify their properties. They were coded for the direction (pro vs. anti), target of the advocacy (e.g., consumption vs. policy), actor demographics (gender, age, and ethnicity), persuasive theme (e.g., excessive sugar, nurturing), and message sensation value. Anti-SSB appeals primarily targeted individual-level consumption behavior. They utilized six persuasive themes and often included more than one theme in a single message. Pro-SSB messages used feel-good themes and utilized only one theme per message. The proportions of adults, adolescents, and children differed by the direction of the advocacy. Black, Hispanic, and Asian actors were under-represented in the anti-SSB sample relative to Whites. Pro-SSB appeals were slightly higher than anti-SSB appeals in message sensation value (p = 0.09). The findings illuminate the message features that characterize the universe of brief anti-SSB appeals available online, highlight messaging disparities, and reveal the absence of certain common, effective persuasive themes.

Drug-Online: an online platform for drug-target interaction, affinity, and binding sites identification using deep learning.

BACKGROUND: Accurately identifying drug-target interaction (DTI), affinity (DTA), and binding sites (DTS) is crucial for drug screening, repositioning, and design, as well as for understanding the functions of target. Although there are a few online platforms based on deep learning for drug-target interaction, affinity, and binding sites identification, there is currently no integrated online platforms for all three aspects. RESULTS: Our solution, the novel integrated online platform Drug-Online, has been developed to facilitate drug screening, target identification, and understanding the functions of target in a progressive manner of "interaction-affinity-binding sites". Drug-Online platform consists of three parts: the first part uses the drug-target interaction identification method MGraphDTA, based on graph neural networks (GNN) and convolutional neural networks (CNN), to identify whether there is a drug-target interaction. If an interaction is identified, the second part employs the drug-target affinity identification method MMDTA, also based on GNN and CNN, to calculate the strength of drug-target interaction, i.e., affinity. Finally, the third part identifies drug-target binding sites, i.e., pockets. The method pt-lm-gnn used in this part is also based on GNN. CONCLUSIONS: Drug-Online is a reliable online platform that integrates drug-target interaction, affinity, and binding sites identification. It is freely available via the Internet at http://39.106.7.26:8000/Drug-Online/ .

Rare earth elements induced electronic engineering in Rh cluster toward efficient alkaline hydrogen evolution reaction.

The unique electronic and crystal structures of rare earth metals (RE) offer promising opportunities for enhancing the hydrogen evolution reaction (HER) properties of materials. In this work, a series of RE (Sm, Nd, Pr and Ho)-doped Rh@NSPC (NSPC stands for N, S co-doped porous carbon nanosheets) with sizes less than 2 nm are prepared, utilizing a simple, rapid and solvent-free joule-heat pyrolysis method for the first time. The optimized Sm-Rh@NSPC achieves HER performance. The high-catalytic performance and stability of Sm-Rh@NSPC are attributed to the synergistic electronic interactions between Sm and Rh clusters, leading to an increase in the electron cloud density of Rh, which promotes the adsorption of H+, the dissociation of Rh-H bonds and the release of H2. Notably, the overpotential of the Sm-Rh@NSPC catalyst is a mere 18.1 mV at current density of 10 mAcm-2, with a Tafel slope of only 15.2 mV dec-1. Furthermore, it exhibits stable operation in a 1.0 M KOH electrolyte at 10 mA cm-2 for more than 100 h. This study provides new insights into the synthesis of composite RE hybrid cluster nanocatalysts and their RE-enhanced electrocatalytic performance. It also introduces fresh perspectives for the development of efficient electrocatalysts.

Incidence, risk factors, and outcomes of the transition of HIPEC-induced acute kidney injury to acute kidney disease: a retrospective study.

BACKGROUND: Acute kidney injury (AKI) is recognized as a common complication following cytoreductive surgery combined with hyperthermic intraperitoneal chemotherapy (CRS-HIPEC). Characterized by prolonged renal function impairment, acute kidney disease (AKD) is associated with a higher risk of chronic kidney disease (CKD) and mortality. METHODS: From January 2018 to December 2021, 158 patients undergoing CRS-HIPEC were retrospectively reviewed. Patients were separated into non-AKI, AKI, and AKD cohorts. Laboratory parameters and perioperative features were gathered to evaluate risk factors for both HIPEC-induced AKI and AKD, with the 90-day prognosis of AKD patients. RESULTS: AKI developed in 21.5% of patients undergoing CRS-HIPEC, while 13.3% progressed to AKD. The multivariate analysis identified that ascites, GRAN%, estimated glomerular filtration rate (eGFR), and intraoperative (IO) hypotension duration were associated with the development of HIPEC-induced AKI. Higher uric acid, lessened eGFR, and prolonged IO hypotension duration were more predominant in patients proceeding with AKD. The AKD cohort presented a higher risk of 30 days of in-hospital mortality (14.3%) and CKD progression (42.8%). CONCLUSIONS: Our study reveals a high incidence of AKI and AKI-to-AKD transition. Early identification of risk factors for HIPEC-induced AKD would assist clinicians in taking measures to mitigate the incidence.