Reconstruction of cancer marker analysis with holistic anatomical precision implicates heterogeneity development during breast tumor progression.

Biomarkers are not only of significant importance for cancer diagnosis and selection of treatment plans but also recently increasingly used for the evaluation of malignancy development and tumor heterogeneity. Large-size tumors from clinical patients can be unique and valuable sources for the study of cancer progression, particularly to the extent of intratumoral heterogeneity. In the present study, we obtained a series of post-surgery puncture samples from a breast cancer patient with a 4 × 3.5 × 2 cm tumor in its original size. Immunohistochemistry for Ki-67, COX-2, and CA IX was performed and the expression levels within the breast cancer tumor mass were evaluated in the reconstructed 3D models. To further evaluate the intratumoral heterogeneity, we performed high throughput whole transcriptome sequencing of 12 samples from different spatial positions within the tumor tissue. Comparing the reconstructed 3D distribution of biomarkers with projected tumor growth models, asymmetric and heterogeneous expansion of tumor mass was found to be possibly influenced by factors such as blood supply, inflammation and/or hypoxia stimulations, as suggested from the correlation between the results of Ki-67 and CA IX or COX-2 staining. Furthermore, high-throughput RNA sequencing data provided additional information for profiling the intratumoral heterogeneity and expanded the understanding of cancer progression. Digital technology for medical imaging once properly integrated with molecular pathology examinations will become particularly helpful in dissecting out in-depth information for precision medicine. We prospect that this approach, facilitated by rapidly advancing artificial intelligence, could provide new insights for clinical decision-making in the future. Strategies for the continuous development from the present study for better performance and application were discussed.

A multiscale distributed neural computing model database (NCMD) for neuromorphic architecture.

Distributed neuromorphic architecture is a promising technique for on-chip processing of multiple tasks. Deploying the constructed model in a distributed neuromorphic system, however, remains time-consuming and challenging due to considerations such as network topology, connection rules, and compatibility with multiple programming languages. We proposed a multiscale distributed neural computing model database (NCMD), which is a framework designed for ARM-based multi-core hardware. Various neural computing components, including ion channels, synapses, and neurons, are encompassed in NCMD. We demonstrated how NCMD constructs and deploys multi-compartmental detailed neuron models as well as spiking neural networks (SNNs) in BrainS, a distributed multi-ARM neuromorphic system. We demonstrated that the electrodiffusive Pinsky-Rinzel (edPR) model developed by NCMD is well-suited for BrainS. All dynamic properties, such as changes in membrane potential and ion concentrations, can be easily explored. In addition, SNNs constructed by NCMD can achieve an accuracy of 86.67% on the test set of the Iris dataset. The proposed NCMD offers an innovative approach to applying BrainS in neuroscience, cognitive decision-making, and artificial intelligence research.

Adaptive fuzzy iterative learning control based neurostimulation system and in-silico evaluation.

Closed-loop neural stimulation has been an effective treatment for epilepsy patients. Currently, most closed-loop neural stimulation strategies are designed based on accurate neural models. However, the uncertainty and complexity of the neural system make it difficult to build an accurate neural model, which poses a significant challenge to the design of the controller. This paper proposes an Adaptive Fuzzy Iterative Learning Control (AFILC) framework for closed-loop neural stimulation, which can realize neuromodulation with no model or model uncertainty. Recognizing the periodic characteristics of neural stimulation and neuronal firing, Iterative Learning Control (ILC) is employed as the primary controller. Furthermore, a fuzzy optimization module is established to update the internal parameters of the ILC controller in real-time. This module enhances the anti-interference ability of the control system and reduces the influence of initial controller parameters on the control process. The efficacy of this strategy is evaluated using a neural computational model. The simulation results validate the capability of the AFILC strategy to suppress epileptic states. Compared with ILC-based closed-loop neurostimulation schemes, the AFILC-based neurostimulation strategy has faster convergence speed and stronger anti-interference ability. Moreover, the control algorithm is implemented based on a digital signal processor, and the hardware-in-the-loop experimental platform is implemented. The experimental results show that the control method has good control performance and computational efficiency, which provides the possibility for future application in clinical research.

Comprehensive reutilization of Glycyrrhiza uralensis residue by extrusion-biological pretreatment for coproduction of flavonoids, cellulase, and ethanol.

A continuous chemical-free green approach was investigated for the comprehensive reutilization of all components in herbal extraction residues (HERs), taking Glycyrrhiza uralensis residue (GUR) as an example. The GUR structural changes induced by mechanical extrusion which improve the specific surface area and enzyme accessibility of GUR. With 3 % pretreated GUR loading of high-tolerance Penicillium oxalicum G2. The reducing sugar yield of 11.45 g/L was achieved, along with an 81.06 % in situ enzymatic hydrolysis. Finally, 8.23 g/L bioethanol (0.40 g/g total sugar) was produced from GUR hydrolysates after 24 h fermentation of Pichia stipitis G32. The amount of functional medicinal ingredients extracted from GUR after hydrolysis (39.63 mg/g) was 37.69 % greater than that of un-pretreated GUR. In total, 1.49 g flavonoids, 294.36 U cellulase, and 14.13 g ethanol could be produced from 100 g GUR using this process, illustrating that this green and efficient process has the potential for industrial production.

Fine-mapping and validation of the major quantitative trait locus QFlANG-4B for flag leaf angle in wheat.

KEY MESSAGE: This study precisely mapped and validated a quantitative trait locus (QTL) located on chromosome 4B for flag leaf angle in wheat. Flag leaf angle (FLANG) is closely related to crop architecture and yield. We previously identified the quantitative trait locus (QTL) QFLANG-4B for FLANG on chromosome 4B, located within a 14-cM interval flanked by the markers Xbarc20 and Xzyh357, using a mapping population of recombinant inbred lines (RILs) derived from a cross between Nongda3331 (ND3331) and Zang1817. In this study, we fine-mapped QFLANG-4B and validated its associated genetic effect. We developed a BC3F3 population using ND3331 as the recurrent parent through marker-assisted selection, as well as near-isogenic lines (NILs) by selfing BC3F3 plants carrying different heterozygous segments for the QFLANG-4B region. We obtained eight recombinant types for QFLANG-4B, narrowing its location down to a 5.3-Mb region. This region contained 76 predicted genes, 7 of which we considered to be likely candidate genes for QFLANG-4B. Marker and phenotypic analyses of individual plants from the secondary mapping populations and their progeny revealed that the FLANG of the ND3331 allele is significantly higher than that of the Zang1817 allele in multiple environments. These results not only provide a basis for the map-based cloning of QFLANG-4B, but also indicate that QFLANG-4B has great potential for marker-assisted selection in wheat breeding programs designed to improve plant architecture and yield.

Scalable Multi-Hierarchy Embedded Platform for Neural Population Simulations.

Brain-inspired structured neural circuits are the cornerstones of both computational and perceived intelligence. Real-time simulations of large-scale high-dimensional neural populations with complex nonlinearities pose a significant challenge. Taking advantage of distributed computations using embedded multi-cores, we propose an ARM-based scalable multi-hierarchy parallel computing platform (EmPaas) for neural population simulations. EmPaas is constructed using 340 ARM Cortex-M4 microprocessors to achieve high-speed and high-accuracy parallel computing. The tree-two-dimensional grid-like hybrid topology completes the overall construction, reducing communication strain and power consumption. As an instance of embedded computing, the optimized model for a biologically plausible basal ganglia-thalamus (BG-TH) network is deployed into this platform to verify the performance. At an operating frequency of 168 MHz, the BG-TH network consisting of 4000 Izhikevich neurons is simulated in the platform for 3000 ms with a power consumption of 56.565 mW per core and an actual time of 2748.57 ms, which shows the parallel computing approach significantly improves computational efficiency. EmPaas can meet the requirement of real-time performance with the maximum amount of 2000 Izhikevich neurons loaded in each Extended Community Unit (ECUnit), which provides a new practical method for research in large-scale brain network simulation and brain-inspired computing.

Type I MADS-box transcription factor TaMADS-GS regulates grain size by stabilizing cytokinin signalling during endosperm cellularization in wheat.

Grain size is one of the important traits in wheat breeding programs aimed at improving yield, and cytokinins, mainly involved in cell division, have a positive impact on grain size. Here, we identified a novel wheat gene TaMADS-GS encoding type I MADS-box transcription factor, which regulates the cytokinins signalling pathway during early stages of grain development to modulate grain size and weight in wheat. TaMADS-GS is exclusively expressed in grains at early stage of seed development and its knockout leads to delayed endosperm cellularization, smaller grain size and lower grain weight. TaMADS-GS protein interacts with the Polycomb Repressive Complex 2 (PRC2) and leads to repression of genes encoding cytokinin oxidase/dehydrogenases (CKXs) stimulating cytokinins inactivation by mediating accumulation of the histone H3 trimethylation at lysine 27 (H3K27me3). Through the screening of a large wheat germplasm collection, an elite allele of the TaMADS-GS exhibits higher ability to repress expression of genes inactivating cytokinins and a positive correlation with grain size and weight, thus representing a novel marker for breeding programs in wheat. Overall, these findings support the relevance of TaMADS-GS as a key regulator of wheat grain size and weight.

Biomarkers Associated with Drugs for the Treatment of Lupus Nephritis.

The constant updating of lupus drug treatment guidelines has led to a question. How can the efficacy of treatment be more effectively monitored? Systemic lupus erythematosus (SLE) is a complex autoimmune disease that often presents clinically with multi-organ involvement, and approximately 30% of patients with SLE develop lupus nephritis (LN). Therefore, it is important to better track disease progression and drug efficacy. Now, kidney biopsy is still the gold standard for diagnosing and guiding the treatment of LN, but it is invasive and expensive. If simple, non-invasive and effective biomarkers can be found, drug intervention and prognosis can be better monitored and targeted. In this review, we focus on LN and explore biomarkers related to LN therapeutics, providing clinicians with more possibilities to track the therapeutic effect of drugs, improve treatment options and assess patient outcomes.

Diagnosing injection-production system faults in the same well using the rough set-LVQ neural network.

This study proposed a reverse calculation model of the unique rod pump injection and production system structures in the same well to diagnose and resolve defects, after which dynamometer diagrams of the system production and injection pumps were drawn. The invariant moment feature method was applied to identify seven such characteristics in the injection pump power graph, establishing a downhole system for fault diagnosis in rod pump injection and production systems in the same well using Rough Set(RS)-Learning Vector Quantization(LVQ). On the premise of keeping the classification ability unchanged, the Self-Organizing Map(SOM) neural network was used to discretize the original feature data, while RS theory was employed for attribute reduction. After establishing the LVQ fault diagnosis subsystem, the reduced decision table was entered for learning and training. The test results confirmed the efficacy and accuracy of this method in diagnosing downhole faults in rod pump injection-production systems in the same well. After comparing the test results with the actual working conditions, it can be seen that the rod pump injection-production diagnosis system based on RS-LVQ designed in this paper has a recognition rate of 91.3% for fault types, strong recognition ability, short diagnosis time, and A certain practicality. However, the research object of fault diagnosis in this paper is a single fault, and the actual downhole fault situation is complex, and there may be two or more fault types at the same time, which has certain limitations.

Stimuli-Actuated Turn-On Theranostic Nanoplatforms for Imaging-Guided Antibacterial Treatment.

Antibacterial theranostic nanoplatforms, which integrate diagnostic and therapeutic properties, exhibit gigantic application prospects in precision medicine. However, traditional theranostic nanoplatforms usually present an always-on signal output, which leads to poor specificity or selectivity in the treatment of bacterial infections. To address this challenge, stimuli-actuated turn-on nanoplatforms are developed for simultaneous activation of diagnostic signals (e.g., fluorescent, photoacoustic, magnetic signals) and initiation of antibacterial treatment. Specifically, by combining the infection microenvironment-responsive activation of visual signals and antibacterial activity, these theranostic nanoplatforms exert both higher accurate diagnosis rates and more effective treatment effects. In this review, the imaging and treatment strategies that are commonly used in the clinic are first briefly introduced. Next, the recent progress of stimuli-actuated turn-on theranostic nanoplatforms for treating bacterial infectious diseases is summarized in detail. Finally, current bottlenecks and future opportunities of antibacterial theranostic nanoplatforms are also outlined and discussed.

BrainS: Customized multi-core embedded multiple scale neuromorphic system.

Research on modeling and mechanisms of the brain remains the most urgent and challenging task. The customized embedded neuromorphic system is one of the most effective approaches for multi-scale simulations ranging from ion channel to network. This paper proposes BrainS, a scalable multi-core embedded neuromorphic system capable of accommodating massive and large-scale simulations. It is designed with rich external extension interfaces to support various types of input/output and communication requirements. The 3D mesh-based topology with an efficient memory access mechanism makes exploring the properties of neuronal networks possible. BrainS operates at 168 MHz and contains a model database ranging from ion channel to network scale within the Fundamental Computing Unit (FCU). At the ion channel scale, the Basic Community Unit (BCU) can perform real-time simulations of a Hodgkin-Huxley (HH) neuron with 16000 ion channels, using 125.54 KB of the SRAM. When the number of ion channels is within 64000, the HH neuron is simulated in real-time by 4 BCUs. At the network scale, the basal ganglia-thalamus (BG-TH) network consisting of 3200 Izhikevich neurons, providing a vital motor regulation function, is simulated in 4 BCUs with a power consumption of 364.8 mW. Overall, BrainS has an excellent performance in real-time and flexible configurability, providing an embedded application solution for multi-scale simulation.

Nonlinear dynamical modeling of neural activity using volterra series with GA-enhanced particle swarm optimization algorithm.

In order to improve the modeling performance of Volterra sequence for nonlinear neural activity, in this paper, a new optimization algorithm is proposed to identify Volterra sequence parameters. Algorithm combines the advantages of particle swarm optimization (PSO) and genetic algorithm (GA) improve the performance of the identification of nonlinear model parameters from rapidity and accuracy. In the modeling experiments of neural signal data generated by the neural computing model and clinical neural data set in this paper, the proposed algorithm shows its excellent potential in nonlinear neural activity modeling. Compared with PSO and GA, the algorithm can achieve less identification error, and better balance the convergence speed and identification error. Further, we explore the influence of algorithm parameters on identification efficiency, which provides possible guiding significance for parameter setting in practical application of the algorithm.

COVID-19 in pulmonary critically ill patients: metagenomic identification of fungi and characterization of pathogenic microorganisms.

Background: Fungal co-infection is prevalent in critically ill patients with COVID-19. The conventional approach applied to fungal identification has relatively low sensitivity and is time-consuming. The metagenomic next-generation sequencing (mNGS) technology can simultaneously detect a variety of microorganisms, and is increasingly being used for the rapid detection and diagnosis of pathogens. Methods: In this single-center retrospective study, we described the clinical presentation and outcomes of COVID-19 and mNGS positive for fungi in pulmonary critically ill patients during the outbreak of Omicron infection from December 2022 to January 2023. Results: Among 43 COVID-19 patients with acute respiratory distress syndrome (ARDS) on a single intensive care unit (ICU), 10 were reported to be fungal positive using the mNGS test. The number of pathogenic microorganisms detected by mNGS was significantly higher than that via traditional methods, especially in the detection of fungi and viruses. Aspergillus infection was dominant, and most of these patients also had concurrent bacterial or viral infections. Probable or possible COVID-19-associated pulmonary aspergillosis (CAPA) was diagnosed in all 10 patients, and the prognosis was poor. Conclusion: Patients with COVID-19 may be at increased risk of developing fungal infections as well as concurrent bacterial or viral infections, and mNGS can be a powerful tool in identifying these infections. Clinicians should be aware of the increased risk of fungal infections in COVID-19 patients, particularly those who have underlying immunocompromising conditions, and should monitor for early signs of infection.

Pinb-D1p is an elite allele for improving end-use quality in wheat (Triticum aestivum L.).

KEY MESSAGE: We identified ten QTLs controlling SDS-SV trait in a RIL population derived from ND3331 and Zang1817. Pinb-D1p is an elite allele from Tibetan semi­wild wheat for good end-use quality. Gluten strength is an important factor for wheat processing and end-product quality and is commonly characterized using the sodium dodecyl sulfate-sedimentation volume (SDS-SV) test. The objective of this study was to identify quantitative trait loci (QTLs) associated with wheat SDS-SV traits using a recombinant inbred line (RIL) population derived from common wheat line NongDa3331 (ND3331) and Tibetan semi-wild wheat accession Zang1817. We detected 10 QTLs controlling SDS-SV on chromosomes 1A, 1B, 3A, 4A, 4B, 5A, 5D, 6B and 7A, with individual QTLs explaining 2.02% to 15.53% of the phenotypic variation. They included four major QTLs, Qsdss-1A, Qsdss-1B.1, Qsdss-1B.2, and Qsdss-5D, whose effects on SDS-SV were due to the Glu-A1 locus encoding the high-molecular-weight glutenin subunit 1Ax1, the 1B/1R translocation, 1Bx7 + 1By8 at the Glu-B1 locus, and the hardness-controlling loci Pina-D1 and Pinb-D1, respectively. We developed KASP markers for the Glu-A1, Glu-B1, and Pinb-D1 loci. Importantly, we showed for the first time that the hardness allele Pinb-D1p positively affects SDS-SV, making it a good candidate for wheat quality improvement. These results broaden our understanding of the genetic characterization of SDS-SV, and the QTLs identified are potential target regions for fine-mapping and marker-assisted selection in wheat breeding programs.

Ferroptosis in inflammatory arthritis: A promising future.

Ferroptosis is a kind of regulatory cell death (RCD) caused by iron accumulation and lipid peroxidation, which is characterized by mitochondrial morphological changes and has a complex regulatory network. Ferroptosis has been gradually emphasized in the pathogenesis of inflammatory arthritis. In this review, we summarized the relevant research on ferroptosis in various inflammatory arthritis including rheumatoid arthritis (RA), osteoarthritis, gout arthritis, and ankylosing spondylitis, and focused on the relationship between RA and ferroptosis. In patients with RA and animal models of RA, there was evidence of iron overload and lipid peroxidation, as well as mitochondrial dysfunction that may be associated with ferroptosis. Ferroptosis inducers have shown good application prospects in tumor therapy, and some anti-rheumatic drugs such as methotrexate and sulfasalazine have been shown to have ferroptosis modulating effects. These phenomena suggest that the role of ferroptosis in the pathogenesis of inflammatory arthritis will be worth further study. The development of therapeutic strategies targeting ferroptosis for patients with inflammatory arthritis may be a promising future.

Biosynthesis of a water solubility-enhanced succinyl glucoside derivative of luteolin and its neuroprotective effect.

The natural flavonoids luteolin and luteoloside have anti-bacterial, anti-inflammatory, anti-oxidant, anti-tumour, hypolipidemic, cholesterol lowering and neuroprotective effects, but their poor water solubility limits their application in industrial production and the pharmaceutical industry. In this study, luteolin-7-O-ß-(6″-O-succinyl)-d-glucoside, a new compound that was prepared by succinyl glycosylation of luteolin by the organic solvent tolerant bacterium Bacillus amyloliquefaciens FJ18 in an 8.0% DMSO (v/v) system, was obtained and identified. Its greater water solubility (2293 times that of luteolin and 12 232 times that of luteoloside) provides the solution to the application problems of luteolin and luteoloside. The conversion rate of luteolin (1.0 g l-1 ) was almost 100% at 24 h, while the yield of luteolin-7-O-ß-(6″-O-succinyl)-d-glucoside reached 76.2%. In experiments involving the oxygen glucose deprivation/reoxygenation injury model of mouse hippocampal neuron cells, the cell viability was significantly improved with luteolin-7-O-ß-(6″-O-succinyl)-d-glucoside dosing, and the expressions of the anti-oxidant enzyme HO-1 in the nucleus increased, providing a neuroprotective effect for ischemic cerebral cells. The availability of biosynthetic luteolin-7-O-ß-(6″-O-succinyl)-d-glucoside, which is expected to replace luteolin and luteoloside, would effectively expand the clinical application value of luteolin derivatives.

Subject-Specific Computational Fluid-Structure Interaction Modeling of Rabbit Vocal Fold Vibration.

A full three-dimensional (3D) fluid-structure interaction (FSI) study of subject-specific vocal fold vibration is carried out based on the previously reconstructed vocal fold models of rabbit larynges. Our primary focuses are the vibration characteristics of the vocal fold, the unsteady 3D flow field, and comparison with a recently developed 1D glottal flow model that incorporates machine learning. The 3D FSI model applies strong coupling between the finite-element model for the vocal fold tissue and the incompressible Navier-Stokes equation for the flow. Five different samples of the rabbit larynx, reconstructed from the magnetic resonance imaging (MRI) scans after the in vivo phonation experiments, are used in the FSI simulation. These samples have distinct geometries and a different inlet pressure measured in the experiment. Furthermore, the material properties of the vocal fold tissue were determined previously for each individual sample. The results demonstrate that the vibration and the intraglottal pressure from the 3D flow simulation agree well with those from the 1D flow model based simulation. Further 3D analyses show that the inferior and supraglottal geometries play significant roles in the FSI process. Similarity of the flow pattern with the human vocal fold is discussed. This study supports the effective usage of rabbit larynges to understand human phonation and will help guide our future computational studies that address vocal fold disorders.

A Real-Time Hardware Experiment Platform for Closed-Loop Electrophysiology.

Targeted stimulation of nervous system has become an increasingly important research tool as well as therapeutic modality, and the stimulation signal acquisition based on the expected signal needs a closed-loop system. Due to the difficulty of biological experiments, the real-time simulation of neural activity is of great significance for the mechanism analysis and the performance improvement of neuromodulation techniques. This paper proposes a real-time hardware experimental platform for closed-loop electrophysiology. The platform integrates a neural computing module and a real-time control module on TMS320F28377D digital signal processors (DSP), and it reserves a programmable interface for users to call the required modules and set module parameters simultaneously. The platform has high compatibility and can be used for closed-loop electrophysiological experiments with different models, different control algorithms and different clamps. We implement the thalamocortical relay neural computing model and iteration improves proportional-integral algorithm on the platform for experimental verification in this paper. The neuron firing waveforms of the DSP platform and the MATLAB R2020b simulation waveforms are consistent. Under the same physiological time, the simulation speed of DSP platform is 3 times faster than that of the Intel Core i5-8400 CPU computer, and the neural firing rate of DSP platform is due to the real-time. This platform can be used as a tool to explore the working mechanism of the nervous system. It may promote the development of neuroscience, especially the field of closed-loop neuroscience.

Identification and stress function verification of the HAK/KUP/KT family in Gossypium hirsutum.

The potassium transporter family HAK/KUP/KT is a large group of proteins that are important in plant potassium transport and play a crucial role in plant growth and development. The members of the family play an important role in the response of plants to abiotic stress by maintaining osmotic balance. However, the function of the family in cotton is unclear. In this study, whole genome identification and characterization of the HAK/KUP/KT family from upland cotton (Gossypium hirsutum) were carried out. Bioinformatics methods were used to identify HAK/KUP/KT family members from the G. hirsutum genome and to analyse the physical and chemical properties, basic characteristics, phylogeny, chromosome location and expression of HAK/KUP/KT family members. A total of 41 HAK/KUP/KT family members were identified in the G. hirsutum genome. Phylogenetic analysis grouped these genes into four clusters (I, II, III, IV), containing 6, 10, 3 and 22 genes, respectively. Chromosomal distribution, gene structure and conserved motif analyses of the 41 GhHAK genes were subsequently performed. The RNA-seq data and qRT-PCR results showed that the family had a wide range of tissue expression patterns, and they responded to certain drought stresses. Through expression analysis, seven HAK/KUP/KT genes involved in drought stress were screened, and four genes with obvious phenotypes under drought stress were obtained by VIGS verification, which laid a theoretical foundation for the function of the cotton HAK/KUP/KT family.

LncRNA OIP5-AS1 reduces renal epithelial cell apoptosis in cisplatin-induced AKI by regulating the miR-144-5p/PKM2 axis.

BACKGROUND: The abnormal expression of long non-coding RNA (lncRNA) Opa-interacting protein 5 antisense RNA 1 (OIP5-AS1) has been observed in many human cancers and the underlying mechanisms have been well studied. However, the function of OIP5-AS1 in acute kidney injury (AKI) remains unclear. METHODS: To explore the role of OIP5-AS1 in the progression of AKI, the cisplatin-induced AKI mouse and cell model were established. To confirm the potential protective effect of OIP5-AS1 during cisplatin-induced AKI, rescue experiments were performed. Targetscan was used to predict the potential targets of miR-144-5p. To further determine whether the effect of miR-144-5p during cisplatin-induced AKI was mediated by PMK2, the recuse experiments using PMK2 overexpressing vector was applied. RESULTS: OIP5-AS1 was significantly downregulated both in cisplatin-induced AKI mice and human renal tubular cell line HK-2 cells. Moreover, overexpression of OIP5-AS1 efficiently promoted cell growth and reduced cisplatin-induced apoptosis of HK-2 cells. Furthermore, OIP5-AS1 was identified as a sponge of miR-144-5p, and upregulation of miR-144-5p could significantly reverse overexpression of OIP5-AS1-induced protective effect on the damage of cisplatin to HK-2 cells. In addition, pyruvate kinase M2 (PKM2) was found to be a direct target of miR-144-5p, and overexpression of PKM2 efficiently reversed the effect of miR-144-5p mimics on the damage in cisplatin-stimulated HK-2 cells. CONCLUSIONS: OIP5-AS1 reduced the apoptosis of cisplatin-stimulated renal epithelial cells by targeting the miR-144-5p/PKM2 axis, which extended the regulatory network of lncRNAs in cisplatin-induced AKI and also provided a novel therapeutic target for AKI treatment.