Drought stress enhances plastid-mediated RNA interference for efficient the willow leaf beetle management.

Plastid-mediated RNA interference has emerged as a promising and effective approach for pest management. By expressing high levels of double-stranded RNAs (dsRNAs) in plastid that target essential pest genes, it has been demonstrated to effectively control certain herbivorous beetles and spider mites. However, as plants are sessile organisms, they frequently experience a combination of biotic and abiotic stresses. It remains unclear whether abiotic stress, such as drought stress, influences the accumulation of dsRNAs produced in plastids and its effectiveness in controlling pests. In this study, we aimed to investigate the effects of drought stress on dsACT expression in transplastomic poplar plants and its control efficiency against the willow leaf beetle (Plagiodera versicolora). Our findings revealed that drought stress did not significantly affect the dsRNA contents in transplastomic poplar plants, but it did lead to higher mortality of insect larvae. This increased mortality may be attributed to increased levels of jasmonic acid and cysteine proteinase inhibitor induced by water deficit. These results contribute to understanding of the mechanisms linking water deficit in plants to insect performance and provide valuable insights for implementing appropriate pest control strategies under drought stress conditions.

Enrichment of novel entomopathogenic Pseudomonas species enhances willow resistance to leaf beetles.

BACKGROUND: Plants have evolved various defense mechanisms against insect herbivores, including the formation of physical barriers, the synthesis of toxic metabolites, and the activation of phytohormone responses. Although plant-associated microbiota influence plant growth and health, whether they play a role in plant defense against insect pests in natural ecosystems is unknown. RESULTS: Here, we show that leaves of beetle-damaged weeping willow (Salix babylonica) trees are more resistant to the leaf beetle Plagiodera versicolora (Coleoptera) than those of undamaged leaves. Bacterial community transplantation experiments demonstrated that plant-associated microbiota from the beetle-damaged willow contribute to the resistance of the beetle-damaged willow to P. versicolora. Analysis of the composition and abundance of the microbiome revealed that Pseudomonas spp. is significantly enriched in the phyllosphere, roots, and rhizosphere soil of beetle-damaged willows relative to undamaged willows. From a total of 49 Pseudomonas strains isolated from willows and rhizosphere soil, we identified seven novel Pseudomonas strains that are toxic to P. versicolora. Moreover, re-inoculation of a synthetic microbial community (SynCom) with these Pseudomonas strains enhances willow resistance to P. versicolora. CONCLUSIONS: Collectively, our data reveal that willows can exploit specific entomopathogenic bacteria to enhance defense against P. versicolora, suggesting that there is a complex interplay among plants, insects, and plant-associated microbiota in natural ecosystems.

Magnetic Properties of Self-Assemble Naphthalene Diimide Radical Aggregates.

The concept of creating room-temperature ferromagnets from organic radicals proposed nearly sixty years ago, has recently experienced a resurgence due to advances in organic radical chemistry and materials. However, the lack of definitive design paradigms for achieving stable long-range ferromagnetic coupling between organic radicals presents an uncertain future for this research. Here, an innovative strategy is presented to achieve room-temperature ferromagnets by assembling π-conjugated radicals into π-π stacking aggregates. These aggregates, with ultra-close π-π distances and optimal π-π overlap, provide a platform for strong ferromagnetic (FM) interaction. The planar aromatic naphthalene diimide (NDI) anion radicals form nanorod aggregates with a π-π distance of just 3.26 Å, shorter than typical van der Waals distances. The suppressed electron paramagnetic resonance (EPR) signal and emergent near-infrared (NIR) absorption of the aggregates confirm strong interactions between the radicals. Magnetic measurements of NDI anion radical aggregates demonstrate room-temperature ferromagnetism with a saturated magnetization of 1.1 emu g-1, the highest among pure organic ferromagnets. Theoretical calculations reveal that π-stacks of NDI anion radicals with specific interlayer translational slippage favor ferromagnetic coupling over antiferromagnetic coupling.

Plasma ofCS-modified CD44 predicts the survival of patients with lung cancer.

Plasma levels of oncofetal chondroitin sulfate (ofCS)-modified CD44 have emerged as a promising biomarker for multi-cancer detection. Here, we explored its potential to predict the survival of patients with lung cancer. A prospective observational cohort was conducted involving 274 newly diagnosed patients with lung cancer at the Sun Yat-sen University Cancer Center from 2013 to 2015. The plasma levels of ofCS-modified CD44 were measured, and Cox regression analysis was performed to assess the association between plasma-modified CD44 levels and overall survival (OS) as well as other prognostic outcomes. Prognostic nomograms were constructed based on plasma ofCS-modified CD44 levels to predict survival outcomes for patients with lung cancer. Patients with high expression ofCS-modified CD44 exhibited significantly worse outcomes in terms of OS (HR = 1.61, 95%CI = 1.13-2.29, p = 0.009) and progression-free survival (PFS). These findings were consistent across various analyses. The concordance index of the prognostic nomogram for predicting OS in both the training set and validation set were 0.723 and 0.737, respectively. Additionally, time-dependent receiver operating characteristic (ROC) curves showed that the nomogram could serve as a useful tool for predicting OS in patients with lung cancer. Plasma ofCS-modified CD44 may serve as an independent prognosis marker for patients with lung cancer. Further validation of its predictive value could enhance prognostic assessment and guide personalized treatment strategies for patients with lung cancer.

SRNet-YOLO: A model for detecting tiny and very tiny pests in cotton fields based on super-resolution reconstruction.

Introduction: Effective pest management is important during the natural growth phases of cotton in the wild. As cotton fields are infested with "tiny pests" (smaller than 32×32 pixels) and "very tiny pests" (smaller than 16×16 pixels) during growth, making it difficult for common object detection models to accurately detect and fail to make sound agricultural decisions. Methods: In this study, we proposed a framework for detecting "tiny pests" and "very tiny pests" in wild cotton fields, named SRNet-YOLO. SRNet-YOLO includes a YOLOv8 feature extraction module, a feature map super-resolution reconstruction module (FM-SR), and a fusion mechanism based on BiFormer attention (BiFormerAF). Specially, the FM-SR module is designed for the feature map level to recover the important feature in detail, in other words, this module reconstructs the P5 layer feature map into the size of the P3 layer. And then we designed the BiFormerAF module to fuse this reconstruct layer with the P3 layer, which greatly improves the detection performance. The purpose of the BiFormerAF module is to solve the problem of possible loss of feature after reconstruction. Additionally, to validate the performance of our method for "tiny pests" and "very tiny pests" detection in cotton fields, we have developed a large dataset, named Cotton-Yellow-Sticky-2023, which collected pests by yellow sticky traps. Results: Through comprehensive experimental verification, we demonstrate that our proposed framework achieves exceptional performance. Our method achieved 78.2% mAP on the "tiny pests" test result, it surpasses the performance of leading detection models such as YOLOv3, YOLOv5, YOLOv7 and YOLOv8 by 6.9%, 7.2%, 5.7% and 4.1%, respectively. Meanwhile, our results on "very tiny pests" reached 57% mAP, which are 32.2% higher than YOLOv8. To verify the generalizability of the model, our experiments on Yellow Sticky Traps (low-resolution) dataset still maintained the highest 92.8% mAP. Discussion: The above experimental results indicate that our model not only provides help in solving the problem of tiny pests in cotton fields, but also has good generalizability and can be used for the detection of tiny pests in other crops.

Telehealth-Supported Exercise or Physical Activity Programs for Knee Osteoarthritis: Systematic Review and Meta-Analysis.

BACKGROUND: The integration of telehealth-supported programs in chronic disease management has become increasingly common. However, its effectiveness for individuals with knee osteoarthritis (KOA) remains unclear. OBJECTIVE: This study aimed to assess the effectiveness of telehealth-supported exercise or physical activity programs for individuals with KOA. METHODS: A comprehensive literature search encompassing Embase, MEDLINE, CENTRAL, Web of Science, PubMed, Scopus, PEDro, GreyNet, and medRxiv from inception to September 2023 was conducted to identify randomized controlled trials comparing telehealth-supported exercise or physical activity programs to a control condition for KOA. Data were extracted and qualitatively synthesized across eligible studies, and a meta-analysis was performed to evaluate the effects. The study was reported according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020. RESULTS: In total, 23 studies met eligibility criteria, with 20 included in the meta-analysis. Results showed that telehealth-supported exercise or physical activity programs reduced pain (g=-0.39; 95% CI -0.67 to -0.11; P<.001), improved physical activity (g=0.13; 95% CI 0.03-0.23; P=.01), and enhanced physical function (g=-0.51; 95% CI -0.98 to -0.05; P=.03). Moreover, significant improvements in quality of life (g=0.25; 95% CI 0.14-0.36; P<.001), self-efficacy for pain (g=0.72; 95% CI 0.53-0.91; P<.001), and global improvement (odds ratio 2.69, 95% CI 1.41-5.15; P<.001) were observed. However, self-efficacy for physical function (g=0.14; 95% CI -0.26 to 0.53; P=.50) showed insignificant improvements. Subgroup analyses based on the World Health Organization classification of digital health (pain: χ22=6.5; P=.04 and physical function: χ22=6.4; P=.04), the type of teletechnology in the intervention group (pain: χ24=4.8; P=.31 and function: χ24=13.0; P=.01), and active or inactive controls (pain: χ21=5.3; P=.02 and physical function: χ21=3.4; P=.07) showed significant subgroup differences. CONCLUSIONS: Telehealth-supported exercise or physical activity programs might reduce knee pain and improve physical activity, physical function, quality of life, self-efficacy, and global improvement in individuals with KOA. Future research should consider longer implementation durations and assess the feasibility of incorporating wearables and standardized components into large-scale interventions to evaluate the effects. TRIAL REGISTRATION: PROSPERO CRD42022359658; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=359658.

An Exact Theory of Causal Emergence for Linear Stochastic Iteration Systems.

After coarse-graining a complex system, the dynamics of its macro-state may exhibit more pronounced causal effects than those of its micro-state. This phenomenon, known as causal emergence, is quantified by the indicator of effective information. However, two challenges confront this theory: the absence of well-developed frameworks in continuous stochastic dynamical systems and the reliance on coarse-graining methodologies. In this study, we introduce an exact theoretic framework for causal emergence within linear stochastic iteration systems featuring continuous state spaces and Gaussian noise. Building upon this foundation, we derive an analytical expression for effective information across general dynamics and identify optimal linear coarse-graining strategies that maximize the degree of causal emergence when the dimension averaged uncertainty eliminated by coarse-graining has an upper bound. Our investigation reveals that the maximal causal emergence and the optimal coarse-graining methods are primarily determined by the principal eigenvalues and eigenvectors of the dynamic system's parameter matrix, with the latter not being unique. To validate our propositions, we apply our analytical models to three simplified physical systems, comparing the outcomes with numerical simulations, and consistently achieve congruent results.

Enhancing the Clinical Utility of Radiomics: Addressing the Challenges of Repeatability and Reproducibility in CT and MRI.

Radiomics, which integrates the comprehensive characterization of imaging phenotypes with machine learning algorithms, is increasingly recognized for its potential in the diagnosis and prognosis of oncological conditions. However, the repeatability and reproducibility of radiomic features are critical challenges that hinder their widespread clinical adoption. This review aims to address the paucity of discussion regarding the factors that influence the reproducibility and repeatability of radiomic features and their subsequent impact on the application of radiomic models. We provide a synthesis of the literature on the repeatability and reproducibility of CT/MR-based radiomic features, examining sources of variation, the number of reproducible features, and the availability of individual feature repeatability indices. We differentiate sources of variation into random effects, which are challenging to control but can be quantified through simulation methods such as perturbation, and biases, which arise from scanner variability and inter-reader differences and can significantly affect the generalizability of radiomic model performance in diverse settings. Four suggestions for repeatability and reproducibility studies are suggested: (1) detailed reporting of variation sources, (2) transparent disclosure of calculation parameters, (3) careful selection of suitable reliability indices, and (4) comprehensive reporting of reliability metrics. This review underscores the importance of random effects in feature selection and harmonizing biases between development and clinical application settings to facilitate the successful translation of radiomic models from research to clinical practice.

Functional connectome gradient predicts clinical symptoms of chronic insomnia disorder.

Insomnia is the second most prevalent psychiatric disorder worldwide, but the understanding of the pathophysiology of insomnia remains fragmented. In this study, we calculated the connectome gradient in 50 chronic insomnia disorder (CID) patients and 38 healthy controls (HC) to assess changes due to insomnia and utilized these gradients in a connectome-based predictive modeling (CPM) to predict clinical symptoms associated with insomnia. The results suggested that insomnia led to significant alterations in the functional gradients of some brain areas. Specifically, the gradient scores in the middle frontal gyrus, superior anterior cingulate gyrus, and right nucleus accumbens were significantly higher in the CID patients than in the HC group, whereas the scores in the middle occipital gyrus, right fusiform gyrus, and right postcentral gyrus were significantly lower than in the HC group. Further correlation analysis revealed that the right middle frontal gyrus is positively correlated with the self-rating anxiety scale (r=0.3702). Additionally, the prediction model built with functional gradients could well predict the sleep quality (r=0.5858), anxiety (r=0.6150), and depression (r=0.4022) levels of insomnia patients. This offers an objective depiction of the clinical diagnosis of insomnia, yielding a beneficial impact on the identification of effective biomarkers and the comprehension of insomnia.

Structures and genetic information of control region in mitogenomes of Odonata.

Mitogenome data of Odonata is accumulating and widely used in phylogenetic analysis. However, noncoding regions, especially control region, were usually omitted from the phylogenetic reconstruction. In an effort to uncover the phylogenetic insights offered by the control region, we have amassed 65 Odonata mitogenomes and conducted an examination of their control regions. Our analysis discovered that species belonging to Anisoptera and Anisozygoptera exhibited a stem-loop structure, which was formed by a conserved polyC-polyG stretch located near the rrns gene (encoding 12S rRNA). Conversely, the polyC-polyG region was not a conserved fragment in Zygoptera. The length and number of repetitions within the control region were identified as the primary determinants of its overall length. Further, sibling species within Odonata, particularly those in the genus Euphaea, displayed similar patterns of repetition in their control region. Collectively, our research delineates the structural variations within the control region of Odonata and suggests the potential utility of this region in elucidating phylogenetic relationships among closely related species.

Efficient control of root-knot nematodes by expressing Bt nematicidal proteins in root leucoplasts.

Root-knot nematodes (RKNs) are plant pests that infect the roots of host plants. Bacillus thuringiensis (Bt) nematicidal proteins exhibited toxicity to nematodes. However, the application of nematicidal proteins for plant protection is hampered by the lack of effective delivery systems in transgenic plants. In this study, we discovered the accumulation of leucoplasts (root plastids) in galls and RKN-induced giant cells. RKN infection causes the degradation of leucoplasts into small vesicle-like structures, which are responsible for delivering proteins to RKNs, as observed through confocal microscopy and immunoelectron microscopy. We showed that different-sized proteins from leucoplasts could be taken up by Meloidogyne incognita female. To further explore the potential applications of leucoplasts, we introduced the Bt crystal protein Cry5Ba2 into tobacco and tomato leucoplasts by fusing it with a transit peptide. The transgenic plants showed significant resistance to RKNs. Intriguingly, RKN females preferentially took up Cry5Ba2 protein when delivered through plastids rather than the cytosol. The decrease in progeny was positively correlated with the delivery efficiency of the nematicidal protein. In conclusion, this study offers new insights into the feeding behavior of RKNs and their ability to ingest leucoplast proteins, and demonstrates that root leucoplasts can be used for delivering nematicidal proteins, thereby offering a promising approach for nematode control.

Comparative Analysis of Repeatability in CT Radiomics and Dosiomics Features under Image Perturbation: A Study in Cervical Cancer Patients.

This study aims to evaluate the repeatability of radiomics and dosiomics features via image perturbation of patients with cervical cancer. A total of 304 cervical cancer patients with planning CT images and dose maps were retrospectively included. Random translation, rotation, and contour randomization were applied to CT images and dose maps before radiomics feature extraction. The repeatability of radiomics and dosiomics features was assessed using intra-class correlation of coefficient (ICC). Pearson correlation coefficient (r) was adopted to quantify the correlation between the image characteristics and feature repeatability. In general, the repeatability of dosiomics features was lower compared with CT radiomics features, especially after small-sigma Laplacian-of-Gaussian (LoG) and wavelet filtering. More repeatable features (ICC > 0.9) were observed when extracted from the original, Large-sigma LoG filtered, and LLL-/LLH-wavelet filtered images. Positive correlations were found between image entropy and high-repeatable feature number in both CT and dose (r = 0.56, 0.68). Radiomics features showed higher repeatability compared to dosiomics features. These findings highlight the potential of radiomics features for robust quantitative imaging analysis in cervical cancer patients, while suggesting the need for further refinement of dosiomics approaches to enhance their repeatability.

Mechanism of discoloration of Antarctic krill oil upon storage: A study based on model systems.

The reddish-orange color of Antarctic krill oil fades during storage, and the mechanism remains unclear. Model systems containing different combinations of astaxanthin (ASTA), phosphatidylethanolamine (PE), and tocopherol were subjected to accelerated storage. Among all groups containing ASTA, only the ones with added PE showed significant fading. Meanwhile, the specific UV-visible absorption (A470 and A495) showed a similar trend. Peroxide value and thiobarbituric acid reactive substances increased during storage, while ASTA and PE contents decreased. Correlation analysis suggested that oxidized PE promoted fading by accelerating the transformation of ASTA. PE content exceeded the critical micelle concentration (1µg/g) indicating the formation of reverse micelles. Molecular docking analysis indicated that PE also interacted with ASTA in an anchor-like manner. Therefore, it is speculated that amphiphilic ASTA is more readily distributed at the oil-water interface of reverse micelles and captured by oxidized PE, which facilitates oxidation transfer, leading to ASTA oxidation and color fading.

Mechanisms and applications of the regenerative capacity of platelets-based therapy in knee osteoarthritis.

Osteoarthritis (OA) is the most prevalent joint disease in the elderly population and its substantial morbidity and disability impose a heavy economic burden on patients and society. Knee osteoarthritis (KOA) is the most common subtype of OA, which is characterized by damage to progressive articular cartilage, synovitis, and subchondral bone sclerosis. Most current treatments for OA are palliative, primarily aim at symptom management, and do not prevent the progression of the disease or restore degraded cartilage. The activation of α-granules in platelets releases various growth factors that are involved in multiple stages of tissue repair, suggesting potential for disease modification. In recent years, platelet-based therapies, such as platelet-rich plasma, platelet-rich fibrin, and platelet lysates, have emerged as promising regenerative treatments for KOA, but their related effects and mechanisms are still unclear. Therefore, this review aims to summarize the biological characteristics and functions of platelets, classify the products of platelet-based therapy and related preparation methods. Moreover, we summarize the basic research of platelet-based regeneration strategies for KOA and discuss the cellular effects and molecular mechanisms. Further, we describe the general clinical application of platelet-based therapy in the treatment of KOA and the results of the meta-analysis of randomized controlled trials.

Nonlinear Regularization Decoding Method for Speech Recognition.

Existing end-to-end speech recognition methods typically employ hybrid decoders based on CTC and Transformer. However, the issue of error accumulation in these hybrid decoders hinders further improvements in accuracy. Additionally, most existing models are built upon Transformer architecture, which tends to be complex and unfriendly to small datasets. Hence, we propose a Nonlinear Regularization Decoding Method for Speech Recognition. Firstly, we introduce the nonlinear Transformer decoder, breaking away from traditional left-to-right or right-to-left decoding orders and enabling associations between any characters, mitigating the limitations of Transformer architectures on small datasets. Secondly, we propose a novel regularization attention module to optimize the attention score matrix, reducing the impact of early errors on later outputs. Finally, we introduce the tiny model to address the challenge of overly large model parameters. The experimental results indicate that our model demonstrates good performance. Compared to the baseline, our model achieves recognition improvements of 0.12%, 0.54%, 0.51%, and 1.2% on the Aishell1, Primewords, Free ST Chinese Corpus, and Common Voice 16.1 datasets of Uyghur, respectively.

Enhancing pathological complete response prediction in breast cancer: the role of dynamic characterization of DCE-MRI and its association with tumor heterogeneity.

BACKGROUND: Early prediction of pathological complete response (pCR) is important for deciding appropriate treatment strategies for patients. In this study, we aimed to quantify the dynamic characteristics of dynamic contrast-enhanced magnetic resonance images (DCE-MRI) and investigate its value to improve pCR prediction as well as its association with tumor heterogeneity in breast cancer patients. METHODS: The DCE-MRI, clinicopathologic record, and full transcriptomic data of 785 breast cancer patients receiving neoadjuvant chemotherapy were retrospectively included from a public dataset. Dynamic features of DCE-MRI were computed from extracted phase-varying radiomic feature series using 22 CAnonical Time-sereis CHaracteristics. Dynamic model and radiomic model were developed by logistic regression using dynamic features and traditional radiomic features respectively. Various combined models with clinical factors were also developed to find the optimal combination and the significance of each components was evaluated. All the models were evaluated in independent test set in terms of area under receiver operating characteristic curve (AUC). To explore the potential underlying biological mechanisms, radiogenomic analysis was implemented on patient subgroups stratified by dynamic model to identify differentially expressed genes (DEGs) and enriched pathways. RESULTS: A 10-feature dynamic model and a 4-feature radiomic model were developed (AUC = 0.688, 95%CI: 0.635-0.741 and AUC = 0.650, 95%CI: 0.595-0.705) and tested (AUC = 0.686, 95%CI: 0.594-0.778 and AUC = 0.626, 95%CI: 0.529-0.722), with the dynamic model showing slightly higher AUC (train p = 0.181, test p = 0.222). The combined model of clinical, radiomic, and dynamic achieved the highest AUC in pCR prediction (train: 0.769, 95%CI: 0.722-0.816 and test: 0.762, 95%CI: 0.679-0.845). Compared with clinical-radiomic combined model (train AUC = 0.716, 95%CI: 0.665-0.767 and test AUC = 0.695, 95%CI: 0.656-0.714), adding the dynamic component brought significant improvement in model performance (train p < 0.001 and test p = 0.005). Radiogenomic analysis identified 297 DEGs, including CXCL9, CCL18, and HLA-DPB1 which are known to be associated with breast cancer prognosis or angiogenesis. Gene set enrichment analysis further revealed enrichment of gene ontology terms and pathways related to immune system. CONCLUSION: Dynamic characteristics of DCE-MRI were quantified and used to develop dynamic model for improving pCR prediction in breast cancer patients. The dynamic model was associated with tumor heterogeniety in prognostic-related gene expression and immune-related pathways.

Individual large-scale functional network mapping for major depressive disorder with electroconvulsive therapy.

Personalized functional connectivity mapping has been demonstrated to be promising in identifying underlying neurophysiological basis for brain disorders and treatment effects. Electroconvulsive therapy (ECT) has been proved to be an effective treatment for major depressive disorder (MDD) while its active mechanisms remain unclear. Here, 46 MDD patients before and after ECT as well as 46 demographically matched healthy controls (HC) underwent resting-state functional magnetic resonance imaging (rs-fMRI) scans. A spatially regularized form of non-negative matrix factorization (NMF) was used to accurately identify functional networks (FNs) in individuals to map individual-level static and dynamic functional network connectivity (FNC) to reveal the underlying neurophysiological basis of therepetical effects of ECT for MDD. Moreover, these static and dynamic FNCs were used as features to predict the clinical treatment outcomes for MDD patients. We found that ECT could modulate both static and dynamic large-scale FNCs at individual level in MDD patients, and dynamic FNCs were closely associated with depression and anxiety symptoms. Importantly, we found that individual FNCs, particularly the individual dynamic FNCs could better predict the treatment outcomes of ECT suggesting that dynamic functional connectivity analysis may be better to link brain functional characteristics with clinical symptoms and treatment outcomes. Taken together, our findings provide new evidence for the active mechanisms and biomarkers for ECT to improve diagnostic accuracy and to guide individual treatment selection for MDD patients.

CD276 enhances sunitinib resistance in clear cell renal cell carcinoma by promoting DNA damage repair and activation of FAK-MAPK signaling pathway.

OBJECTIVE: This study aimed to explore the effect of CD276 expression on the sunitinib sensitivity of clear cell renal cell carcinoma (ccRCC) cell and animal models and the potential mechanisms involved. METHODS: CD276 expression levels of ccRCC and normal samples were analyzed via online databases and real-time quantitative PCR (RT-qPCR). CD276 was knocked down in ccRCC cell models (sunitinib-resistant 786-O/R cells and sunitinib-sensitive 786-O cells) using shRNA transfection, and the cells were exposed to a sunitinib (2 µM) environment. Cells proliferation was then analyzed using MTT assay and colony formation experiment. Alkaline comet assay, immunofluorescent staining, and western blot experiments were conducted to assess the DNA damage repair ability of the cells. Western blot was also used to observe the activation of FAK-MAPK pathway within the cells. Finally, a nude mouse xenograft model was established and the nude mice were orally administered sunitinib (40 mg/kg/d) to evaluate the in vivo effects of CD276 knockdown on the therapeutic efficacy of sunitinib against ccRCC. RESULTS: CD276 was significantly upregulated in both ccRCC clinical tissue samples and cell models. In vitro experiments showed that knocking down CD276 reduced the survival rate, IC50 value, and colony-forming ability of ccRCC cells. Knocking down CD276 increased the comet tail moment (TM) values and γH2AX foci number, and reduced BRCA1 and RAD51 protein levels. Knocking down CD276 also decreased the levels of p-FAK, p-MEK, and p-ERK proteins. CONCLUSION: Knocking down CD276 effectively improved the sensitivity of ccRCC cell and animal models to sunitinib treatment.