Fluorescence labeling-based differential scanning fluorimetry, an effective method for protein thermal stability and protein-compound binding analysis.

Differential scanning fluorimetry (DSF) is widely used to assess protein thermal stability and protein-ligand interaction. However, its utility is often limited by the presence of detergents, which can affect hydrophobic binding. To tackle this issue, we developed an effective fluorescence-labeled DSF (FL-DSF) technique that tracks protein denaturation by monitoring the labeling fluorescence decrease, thus overcoming challenges typically encountered with traditional DSF methods. In this research, FL-DSF was first validated using Peroxisome Proliferators-Activated Receptor γ (PPARγ), Retinoid X Receptor α (RXRα), and Lysozyme, confirming its accuracy in determining melting curves. Expectedly, FL-DSF also exhibited strong compatibility with detergents in our investigations. Besides this, a new calculation method was proposed to characterize the protein denaturation process and evaluate protein-ligand binding. This mathematical model goes beyond traditional approaches, which simply treated the melting temperature (TM) shift as a concentration-dependent variable. Instead, it comprehensively incorporates the influence of irreversible denaturation-induced native protein loss on the equilibrium of protein-ligand binding. This methodology was successfully applied into the evaluation of binding affinity for 2 classical binding systems of PPARγ-Rosiglitazone and RXRα-CD3254. It was also utilized for the following binding screening studies, leading to the discovery of promising ligands for PPARγ, RXRα, and Lysozyme.

Granular Porous Nanofibrous Microspheres Enhance Cellular Infiltration for Diabetic Wound Healing.

Diabetic foot ulcers (DFUs) are a significant challenge in the clinical care of diabetic patients, often necessitating limb amputation and compromising the quality of life and life expectancy of this cohort. Minimally invasive therapies, such as modular scaffolds, are at the forefront of current DFU treatment, offering an efficient approach for administering therapeutics that accelerate tissue repair and regeneration. In this study, we report a facile method for fabricating granular nanofibrous microspheres (NMs) with predesigned structures and porosities. The proposed technology combines electrospinning and electrospraying to develop a therapeutic option for DFUs. Specifically, porous NMs were constructed using electrospun poly(lactic-co-glycolic acid) (PLGA):gelatin short nanofibers, followed by gelatin cross-linking. These NMs demonstrated enhanced cell adhesion to human dermal fibroblasts (HDF) during an in vitro cytocompatibility assessment. Notably, porous NMs displayed superior performance owing to their interconnected pores compared to nonporous NMs. Cell-laden NMs demonstrated higher Young's modulus values than NMs without loaded cells, suggesting improved material resiliency attributed to the reinforcement of cells and their secreted extracellular matrix. Dynamic injection studies on cell-laden NMs further elucidated their capacity to safeguard loaded cells under pressure. In addition, porous NMs promoted host cell infiltration, neovascularization, and re-epithelialization in a diabetic mouse wound model, signifying their effectiveness in healing diabetic wounds. Taken together, porous NMs hold significant potential as minimally invasive, injectable treatments that effectively promote tissue integration and regeneration.

PotatoG-DKB: a potato gene-disease knowledge base mined from biological literature.

Background: Potato is the fourth largest food crop in the world, but potato cultivation faces serious threats from various diseases and pests. Despite significant advancements in research on potato disease resistance, these findings are scattered across numerous publications. For researchers, obtaining relevant knowledge by reading and organizing a large body of literature is a time-consuming and labor-intensive process. Therefore, systematically extracting and organizing the relationships between potato genes and diseases from the literature to establish a potato gene-disease knowledge base is particularly important. Unfortunately, there is currently no such gene-disease knowledge base available. Methods: In this study, we constructed a Potato Gene-Disease Knowledge Base (PotatoG-DKB) using natural language processing techniques and large language models. We used PubMed as the data source and obtained 2,906 article abstracts related to potato biology, extracted entities and relationships between potato genes and related disease, and stored them in a Neo4j database. Using web technology, we also constructed the Potato Gene-Disease Knowledge Portal (PotatoG-DKP), an interactive visualization platform. Results: PotatoG-DKB encompasses 22 entity types (such as genes, diseases, species, etc.) of 5,206 nodes and 9,443 edges between entities (for example, gene-disease, pathogen-disease, etc.). PotatoG-DKP can intuitively display associative relationships extracted from literature and is a powerful assistant for potato biologists and breeders to understand potato pathogenesis and disease resistance. More details about PotatoG-DKP can be obtained at https://www.potatogd.com.cn/.

A novel framework for uncovering the coordinative spectrum-effect correlation of the effective components of Yangyin Tongnao Granules on cerebral ischemia-reperfusion injury in rats.

ETHNOPHARMACOLOGICAL RELEVANCE: Ischemic stroke is currently a major public health hazard.Yangyin Tongnao Granules (YYTN), a traditional Chinese medicinal prescription, exerts potential therapeutic effects on subsequent cerebral ischemia-reperfusion injury (CIRI) after ischemic stroke. However, further studies are required to comprehend the underlying mechanism of YYTN for treating CIRI and the associated spectrum-effect mechanisms. AIM OF THE STUDY: To investigate the coordinated correlation between the fingerprint and the pharmacodynamic indexes of the effective components (total flavonoids, total saponins, total alkaloids, and total phenolic acids) in YYTN for treating CIRI in rats. METHODS: The fingerprints of five specific components (ligustrazine, puerarin, ferulic acid, calycosin, and formononetin) of YYTN in rats with middle cerebral artery occlusion (MCAO) were established using high-performance liquid chromatography (HPLC), and their peak areas were quantified in plasma samples. The pharmacodynamic indexes of tumor necrosis factor-alpha (TNF-α), cytochrome c (Cyt-C), and total superoxide dismutase (T-SOD) were integrated using the Criteria Importance Through Intercriteria Correlation (CRITIC) method to create a comprehensive evaluation index. Spectrum-effect correlation was analyzed by performing gray relation analysis (GRA), correlation analysis (CA), and partial least squares regression (PLSR). The Borda method was then applied to integrate the obtained results. RESULTS: In MCAO rats, the effective components of YYTN reduced TNF-α and Cyt-C and increased T-SOD, which indicates their anti-inflammatory, antiapoptotic, and antioxidant effects. Spectrum-effect CA revealed certain associations between the chromatographic peaks of the five main components and the comprehensive pharmacodynamic evaluation index. Of these components, formononetin displayed the highest correlation, whereas ferulic acid exhibited the lowest correlation. All components showed a positive correlation. Using the Borda method, the components were ranked as follows based on correlation: formononetin > calycosin > ligustrazine > puerarin > ferulic acid. CONCLUSIONS: The effective components of YYTN exhibited synergistic effects in the treatment of MCAO rats, which could potentially be attributed to their multitarget and multipathway mechanisms. The Borda method-based spectrum-effect correlation analysis provides a coordinated approach to investigate the relationship between fingerprint and pharmacodynamics of traditional Chinese medicine (TCM).

Acustimulation combined with pharmacological prophylaxis versus pharmacological prophylaxis alone in postoperative nausea and vomiting (PONV) prophylaxis among patients undergoing laparoscopy abdominal surgery: a research protocol for a randomised controlled trial.

INTRODUCTION: This study evaluates the efficacy of integrating percutaneous electrical nerve stimulation at the pericardium 6 (PC6) acupuncture point through a wearable acustimulation device with standard pharmacological prophylaxis to prevent postoperative nausea and vomiting (PONV) compared with pharmacological prophylaxis alone in patients undergoing laparoscopic abdominal surgery. METHOD AND ANALYSIS: This prospective study will enrol 302 patients scheduled for elective laparoscopic surgery. Participants will be randomly allocated to one of two groups: acustimulation combined with pharmacological prophylaxis or sham stimulation combined with pharmacological prophylaxis. Randomisation will involve a computer-generated sequence, with allocation concealment implemented through sealed envelopes. The acustimulation group will receive electrical stimulation at the PC6 point starting 30 min before surgery and continuing until discharge from the postanaesthesia care unit. Sham group will wear a wristband that does not provide stimulation. The primary outcome is the incidence of PONV. Secondary outcomes include the severity of PONV, incidence rates of nausea and vomiting at different postoperative intervals and indices of gastrointestinal functional recovery. Exploratory outcomes will assess haemodynamic parameters, baroreflex sensitivity, hospital stay duration, costs and both short-term and long-term postoperative recovery. ETHICS AND DISSEMINATION: All participants will provide written informed consent. The study has been approved by the Ethics Committee of Peking Union Medical Hospital (approval number: I-23PJ1712). Results will be published open access. TRIAL REGISTRATION NUMBER: NCT06241547.

[3D bioprinting: classification, evaluation, and application in vascular tissue engineering].

Cardiovascular diseases are major diseases, and there is lack of artificial blood vessels with small diameters which can be applied in coronary artery bypass surgery. The conventional vascular scaffold preparation techniques in tissue engineering have shortcomings in regulating the diameter, geometric shape, and interconnectivity of the scaffold. 3D bioprinting can simulate the natural structure of the vascular tissue, accurately print live cells and biomaterials, and regulate the microstructure and porosity of scaffolds on the nanoscale, providing new ideas for vascular tissue engineering. This article systematically evaluates the classification of 3D bioprinting technologies and reviews the latest research progress of 3D bioprinting in vascular tissue engineering. It summarizes the advantages of 3D bioprinting and points out the problems that need to be solved, such as the immune rejection of blood vessel materials, providing reference for the further research.

Machine learning based androgen receptor regulatory gene-related random forest survival model for precise treatment decision in prostate cancer.

Background: It has been demonstrated that aberrant androgen receptor (AR) signaling contributes to the pathogenesis of prostate cancer (PCa). To date, the most efficacious strategy for the treatment of PCa remains to target the AR signaling axis. However, numerous PCa patients still face the issue of overtreatment or undertreatment. The establishment of a precise risk prediction model is urgently needed to distinguish patients with high-risk and select appropriate treatment modalities. Methods: In this study, a consensus AR regulatory gene-related signature (ARS) was developed by integrating a total of 101 algorithm combinations of 10 machine learning algorithms. We evaluated the value of ARS in predicting patient prognosis and the therapeutic effects of the various treatments. Additionally, we conducted a screening of therapeutic targets and agents for high-risk patients, followed by the verification in vitro and in vivo. Results: ARS was an independent risk factor for biochemical recurrence and distant metastasis in PCa patients. The enhanced and consistent prognostic predictive capability of ARS across various platforms was confirmed when compared with 44 previously published signatures. More importantly, PCa patients in the ARShigh group benefit more from PARP inhibitors and immunotherapy, while chemotherapy, radiotherapy, and AR-targeted therapy are more effective for ARSlow patients. The results of in silico screening suggest that AURKB could potentially serve as a promising therapeutic target for ARShigh patients. Conclusions: Collectively, this prediction model based on AR regulatory genes holds great clinical translational potential to solve the dilemma of treatment choice and identify potential novel therapeutic targets in PCa.

Integrating large-scale single-cell RNA sequencing in central nervous system disease using self-supervised contrastive learning.

The central nervous system (CNS) comprises a diverse range of brain cell types with distinct functions and gene expression profiles. Although single-cell RNA sequencing (scRNA-seq) provides new insights into the brain cell atlases, integrating large-scale CNS scRNA-seq data still encounters challenges due to the complexity and heterogeneity among CNS cell types/subtypes. In this study, we introduce a self-supervised contrastive learning method, called scCM, for integrating large-scale CNS scRNA-seq data. scCM brings functionally related cells close together while simultaneously pushing apart dissimilar cells by comparing the variations of gene expression, effectively revealing the heterogeneous relationships within the CNS cell types/subtypes. The effectiveness of scCM is evaluated on 20 CNS datasets covering 4 species and 10 CNS diseases. Leveraging these strengths, we successfully integrate the collected human CNS datasets into a large-scale reference to annotate cell types and subtypes in neural tissues. Results demonstrate that scCM provides an accurate annotation, along with rich spatial information of cell state. In summary, scCM is a robust and promising method for integrating large-scale CNS scRNA-seq data, enabling researchers to gain insights into the cellular and molecular mechanisms underlying CNS functions and diseases.

Optimal methods for analyzing targeted pairwise knockout screens.

Background: Synthetic lethality offers a promising strategy for cancer treatment by targeting genetic vulnerabilities unique to tumor cells, leading to selective tumor cell death. However, single-gene knockout screens often miss functional redundancy due to paralog genes. Multiplex CRISPR systems, including various Cas9 and Cas12a platforms, have been developed to assay genetic interactions, yet no systematic comparison of method to identify synthetic lethality from CRISPR screens has been conducted. Results: We evaluated data from four in4mer CRISPR/Cas12a screens in cancer cell lines, using three bioinformatic approaches to identify synthetic lethal interactions: delta log fold change (dLFC), Z-transformed dLFC (ZdLFC), and rescaled dLFC (RdLFC). Both ZdLFC and RdLFC provided more consistent identification of synthetic lethal pairs across cell lines compared to the unscaled dLFC method. Conclusions: The ZdLFC method offers a robust framework for scoring synthetic lethal interactions from paralog screens, providing consistent results across different cell lines without requiring a training set of known positive interactors.

Filicinic acid based meroterpenoids from Hypericum elodeoides and their anti-Alzheimer's disease effects.

(±)-Elodeoidileons A-L (1-12), 12 pairs of previously undescribed filicinic acid based meroterpenoids were isolated from Hypericum elodeoides with unique linear or angular 6/6/6 ring core. Modern spectroscopic techniques, modified Mosher's method and quantum chemical calculations were used to identify the planner structures and configurations of 1-12. Additionally, the potential biosynthetic pathways for 1-12 were anticipated. Moreover, biological activity assessments suggested that 1a, 5a, and 11b could activate Retinoid X receptor-α (RXRα) transcription and enhance the ATP-binding cassette transporter A1 (ABCA1) protein's expression. Fluorescence titration assay suggested that 1a might have a direct interaction with the RXRα-LBD protein, with an estimated Kd value of 5.85 µM. Moreover, molecular docking study confirmed the binding of 1a to RXRα and further validated by cellular thermal shift assay (CETSA). Thus, compound 1a may promote ß-amyloid (Aß) clearance by targeting RXRα and upregulating the expression of the ABCA1 protein, showing promise as anti-Alzheimer's agent.

Evaluating Performance of Different RNA Secondary Structure Prediction Programs Using Self-cleaving Ribozymes.

Accurate identification of the correct, biologically relevant RNA structures is critical to understanding various aspects of RNA biology since proper folding represents the key to the functionality of all types of RNA molecules and plays pivotal roles in many essential biological processes. Thus, a plethora of approaches have been developed to predict, identify, or solve RNA structures based on various computational, molecular, genetic, chemical, or physicochemical strategies. Purely computational approaches hold distinct advantages over all other strategies in terms of the ease of implementation, time, speed, cost, and throughput, but they strongly underperform in terms of accuracy that significantly limits their broader application. Nonetheless, the advantages of these methods led to a steady development of multiple in silico RNA secondary structure prediction approaches including recent deep learning-based programs. Here, we compared the accuracy of predictions of biologically relevant secondary structures of dozens of self-cleaving ribozyme sequences using seven in silico RNA folding prediction tools with tasks of varying complexity. We found that while many programs performed well in relatively simple tasks, their performance varied significantly in more complex RNA folding problems. However, in general, a modern deep learning method outperformed the other programs in the complex tasks in predicting the RNA secondary structures, at least based on the specific class of sequences tested, suggesting that it may represent the future of RNA structure prediction algorithms.

Reducing the risk of HIV/AIDS transmission using intervention mapping: a systematic review.

Intervention mapping (IM) is a planning approach that reflects the intricate decision-making process involved in the design of behavior interventions. The development and implementation of IM is complex in preventing HIV/AIDS transmission. Therefore, it is significant to conduct a perfect preliminary work to successfully implement HIV/AIDS prevention. The objectives of this review were to collect and evaluate the data of the first three steps using IM to prevent HIV/AIDS transmission, and summarize the key points in the preliminary steps of IM. A total of 18 studies were identified, and six studies completely described the tasks in the first three steps of IM. Three studies described the logic model of the problem (n = 3). Six studies reported the matrix of behavior changes (n = 6), including personal and environmental determinants. Among the selected determinants, most studies reported the personal level determinants (self-efficacy and skills, knowledge, attitudes, and norms). The most used practical applications in reducing HIV/AIDS risk behaviors were video roles (n = 8) and role-model stories (n = 5). The review may be helpful for healthcare professionals to carefully design and implement the key procedures of the first three steps of the IM programs for people with HIV/AIDS in preventing HIV/AIDS transmission.

In vivo CRISPR screens identify Mga as an immunotherapy target in triple-negative breast cancer.

Immune evasion is not only critical for tumor initiation and progression, but also determines the efficacy of immunotherapies. Through iterative in vivo CRISPR screens with seven syngeneic tumor models, we identified core and context-dependent immune evasion pathways across cancer types. This valuable high-confidence dataset is available for the further understanding of tumor intrinsic immunomodulators, which may lead to the discovery of effective anticancer therapeutic targets. With a focus on triple-negative breast cancer (TNBC), we found that Mga knock-out significantly enhances antitumor immunity and inhibits tumor growth. Transcriptomics and single-cell RNA sequencing analyses revealed that Mga influences various immune-related pathways in the tumor microenvironment. Our findings suggest that Mga may play a role in modulating the tumor immune landscape, though the precise mechanisms require further investigation. Interestingly, we observed that low MGA expression in breast cancer patients correlates with a favorable prognosis, particularly in those with active interferon-γ signaling. These observations provide insights into tumor immune escape mechanisms and suggest that further exploration of MGA's function could potentially lead to effective therapeutic strategies in TNBC.

Metabolic tumor volume from baseline [18 F]FDG PET/CT at diagnosis improves the IPI stratification in patients with diffuse large B-cell lymphoma.

PURPOSE: Although several different parameters of PET/CT were reported to be predictive of survival in DLBCL, the best parameter remains to be elucidated and whether it could improve the risk stratification of IPI in patients with DLBCL. PROCEDURES: 262 DLBCL patients including in the training and validation cohort were retrospectively analyzed in this study. RESULTS: Among different parameters, MTV was identified as the optimal prognostic parameter with a maximum area under the curve (AUC) of 0.652 ± 0.112 than TLG and SDmax (0.645 ± 0.113 and 0.600 ± 0.117, respectively). Patients with high MTV were associated with inferior PFS (p < 0.001 and p = 0.021, respectively) and OS (p < 0.001 and p < 0.001, respectively) in both the training and validation cohort. The multivariate analysis revealed that high MTV was an unfavorable factor for PFS (relative ratio [RR], 2.295; 95% confidence interval [CI], 1.457-3.615; p < 0.01) and OS (RR, 2.929; 95% CI 1.679-5.109; p < 0.01) independent of IPI. CONCLUSIONS: Further analysis showed MTV could improve the risk stratification of IPI for both PFS and OS (p < 0.01 and p < 0.01, respectively). In conclusion, our study suggests that MTV was an optimal prognostic parameter of PET/CT for survival and it could improve the risk stratification of IPI in DLBCL, which may help to guide treatment in clinical trial.

The use of ectopic volar fibroblasts to modify skin identity.

Skin identity is controlled by intrinsic features of the epidermis and dermis and their interactions. Modifying skin identity has clinical potential, such as the conversion of residual limb and stump (nonvolar) skin of amputees to pressure-responsive palmoplantar (volar) skin to enhance prosthesis use and minimize skin breakdown. Greater keratin 9 (KRT9) expression, higher epidermal thickness, keratinocyte cytoplasmic size, collagen length, and elastin are markers of volar skin and likely contribute to volar skin resiliency. Given fibroblasts' capacity to modify keratinocyte differentiation, we hypothesized that volar fibroblasts influence these features. Bioprinted skin constructs confirmed the capacity of volar fibroblasts to induce volar keratinocyte features. A clinical trial of healthy volunteers demonstrated that injecting volar fibroblasts into nonvolar skin increased volar features that lasted up to 5 months, highlighting a potential cellular therapy.

Risk predictive model for the development of hepatocellular carcinoma before initiating long-term antiviral therapy in patients with chronic hepatitis B virus infection.

It is generally acknowledged that antiviral therapy can reduce the incidence of hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC), there remains a subset of patients with chronic HBV infection who develop HCC despite receiving antiviral treatment. This study aimed to develop a model capable of predicting the long-term occurrence of HCC in patients with chronic HBV infection before initiating antiviral therapy. A total of 1450 patients with chronic HBV infection, who received initial antiviral therapy between April 2006 and March 2023 and completed long-term follow-ups, were nonselectively enrolled in this study. Least absolute shrinkage and selection operator (LASSO) and Cox regression analysis was used to construct the model. The results were validated in an external cohort (n = 210) and compared with existing models. The median follow-up time for all patients was 60 months, with a maximum follow-up time of 144 months, during which, 32 cases of HCC occurred. The nomogram model for predicting HCC based on GGT, AFP, cirrhosis, gender, age, and hepatitis B e antibody (TARGET-HCC) was constructed, demonstrating a good predictive performance. In the derivation cohort, the C-index was 0.906 (95% CI = 0.869-0.944), and in the validation cohort, it was 0.780 (95% CI = 0.673-0.886). Compared with existing models, TARGET-HCC showed promising predictive performance. Additionally, the time-dependent feature importance curve indicated that gender consistently remained the most stable predictor for HCC throughout the initial decade of antiviral therapy. This simple predictive model based on noninvasive clinical features can assist clinicians in identifying high-risk patients with chronic HBV infection for HCC before the initiation of antiviral therapy.

Mechanistic Interrogation on Wound Healing and Scar Removing by the Mo4/3B2-x Nanoscaffold Revealed Regulated Amino Acid and Purine Metabolism.

Wound rehabilitation is invariably time-consuming, scar formation further weakens therapeutic efficacy, and detailed mechanisms at the molecular level remain unclear. In this work, a Mo4/3B2-x nanoscaffold was fabricated and utilized for wound healing and scar removing in a mice model, while metabolomics was used to study the metabolic reprogramming of metabolome during therapy at the molecular level. The results showed that transition metal borides, called Mo4/3B2-x nanoscaffolds, could mimic superoxide dismutase and glutathione peroxidase to eliminate excess reactive oxygen species (ROS) in the wound microenvironment. During the therapeutic process, the Mo4/3B2-x nanoscaffold could facilitate the regeneration of wounds and removal of scars by regulating the biosynthesis of collagen, fibers, and blood vessels at the pathological, imaging, and molecular levels. Subsequent metabolomics study revealed that the Mo4/3B2-x nanoscaffold effectively ameliorated metabolic disorders in both wound and scar microenvironments through regulating ROS-related pathways including the amino acid metabolic process (including glycine and serine metabolism and glutamate metabolism) and the purine metabolic process. This study is anticipated to illuminate the potential clinical application of the Mo4/3B2-x nanoscaffold as an effective therapeutic agent in traumatic diseases and provide insights into the development of analytical methodology for interrogating wound healing and scar removal-related metabolic mechanisms.

The Mitochondrial Distribution and Morphology Family 33 Gene FgMDM33 Is Involved in Autophagy and Pathogenesis in Fusarium graminearum.

The mitochondrial distribution and morphology family 33 gene (MDM33) regulates mitochondrial homeostasis by mediating the mitochondrial fission process in yeast. The wheat head blight Fusarium graminearum contains an FgMdm33 protein that is orthologous to Saccharomyces cerevisiae Mdm33, albeit its function remains unknown. We have reported here the roles of FgMdm33 in regulating fungal morphogenesis, mitochondrial morphology, autophagy, apoptosis, and fungal pathogenicity. The ΔFgmdm33 mutants generated through a homologous recombination strategy in this study exhibited defects in terms of mycelial growth, conidia production, and virulence. Hyphal cells lacking FgMDM33 displayed elongated mitochondria and a dispensable respiratory-deficient growth phenotype, indicating the possible involvement of FgMDM33 in mitochondrial fission. The ΔFgmdm33 mutants displayed a remarkable reduction in the proteolysis of GFP-FgAtg8, whereas the formation of autophagic bodies in the hyphal cells of mutants was recorded under the induction of mitophagy. In addition, the transcriptional expression of the apoptosis-inducing factor 1 gene (FgAIF1) was significantly upregulated in the ΔFgmdm33 mutants. Cumulatively, these results indicate that FgMDM33 is involved in mitochondrial fission, non-selective macroautophagy, and apoptosis and that it regulates fungal growth, conidiation, and pathogenicity of the head blight pathogen.

Piezoelectric Effect-Mediating Reactive Oxygen Species in NiTiO3 Nanorods for Photocatalytic Removal of U(VI).

Piezoelectric catalysis could convert mechanical energy into chemical energy, which can combine with solar energy for a high-efficiency piezo-photocatalysis reaction. In this work, NiTiO3 nanorods were synthesized via the sol-gel method and initially employed for the removal of U(VI) from radioactive-contaminated water. The NiTiO3 nanorods will generate an internal electric field in an ultrasonic environment, which could greatly improve the performance of piezo-photocatalysis in reducing U(VI) by promoting the generation of photoexcited electrons and reactive oxygen species (ROS). After exposure to visible light and ultrasound for 3 h, the NTO-R-1 exhibited superb U(VI) degradation efficiency of 93.91%, which was 2.58, 6.15, and 6.68 times greater than those of visible light, ultrasonic irradiation, and dark, respectively. Moreover, photoexcited electrons and oxygen-active species play a decisive role in the piezo-photocatalysis process. Therefore, NiTiO3 with excellent piezo-photocatalysis properties exhibits good potential for the development of efficient wastewater purification catalysts and also helps to probe the possible mechanism of piezo-photocatalysis removal of U(VI) in wastewater.