Prediction value study of breast cancer tumor infiltrating lymphocyte levels based on ultrasound imaging radiomics.

Objective: Construct models based on grayscale ultrasound and radiomics and compare the efficacy of different models in preoperatively predicting the level of tumor-infiltrating lymphocytes in breast cancer. Materials and methods: This study retrospectively collected clinical data and preoperative ultrasound images from 185 breast cancer patients confirmed by surgical pathology. Patients were randomly divided into a training set (n=111) and a testing set (n=74) using a 6:4 ratio. Based on a 10% threshold for tumor-infiltrating lymphocytes (TIL) levels, patients were classified into low-level and high-level groups. Radiomic features were extracted and selected using the training set. The evaluation included assessing the relationship between TIL levels and both radiomic features and grayscale ultrasound features. Subsequently, grayscale ultrasound models, radiomic models, and nomograms combining radiomics score (Rad-score) and grayscale ultrasound features were established. The predictive performance of different models was evaluated through receiver operating characteristic (ROC) analysis. Calibration curves assessed the fit of the nomograms, and decision curve analysis (DCA) evaluated the clinical effectiveness of the models. Results: Univariate analyses and multivariate logistic regression analyses revealed that indistinct margin (P<0.001, Odds Ratio [OR]=0.214, 95% Confidence Interval [CI]: 0.103-1.026), posterior acoustic enhancement (P=0.027, OR=2.585, 95% CI: 1.116-5.987), and ipsilateral axillary lymph node enlargement (P=0.001, OR=4.214, 95% CI: 1.798-9.875) were independent predictive factors for high levels of TIL in breast cancer. In comparison to grayscale ultrasound model (Training set: Area under curve [AUC] 0.795; Testing set: AUC 0.720) and radiomics model (Training set: AUC 0.803; Testing set: AUC 0.759), the nomogram demonstrated superior discriminative ability on both the training (AUC 0.884) and testing (AUC 0.820) datasets. Calibration curves indicated high consistency between the nomogram model's predicted probability of breast cancer TIL levels and the actual occurrence probability. DCA revealed that the radiomics model and the nomogram model achieved higher clinical net benefits compared to the grayscale ultrasound model. Conclusion: The nomogram based on preoperative ultrasound radiomics features exhibits robust predictive capacity for the non-invasive evaluation of breast cancer TIL levels, potentially providing a significant basis for individualized treatment decisions in breast cancer.

A 0D-3D multi-scale model of the portal venous system coupled with the entire cardiovascular system applied to predict postsplenectomy hemodynamic metrics.

Splenectomy is a common surgery for portal hypertensive patients with splenomegaly. Although splenectomy is able to effectively relieve the complications of portal hypertension, it also increases the risk of portal venous system thrombosis remarkably. Previous studies demonstrated that the hemodynamic metrics of the portal venous system could be employed in predicting the risk of postsplenectomy thrombosis, and 3D models were utilized to simulate the blood flow in the portal venous system. Aiming to reflect the global effect of splenectomy and better simulate the hemodynamic metrics, in this study, a 0D-3D multi-scale model of the portal venous system coupled with the entire cardiovascular system was constructed based on population-averaged data in combination with patient-specific preoperative clinical measurements. The pre- and postoperative global blood flows as well as the variations were calculated successfully, and the flow field and time-averaged wall shear stress of the portal venous system were simulated. The model-simulated spatial distributions of the hemodynamic metrics in the portal venous system were comparable with the regions suffering from thrombosis after splenectomy. These results imply that the present model could reflect the reallocation of the blood flow in the splanchnic circulation after splenectomy and simulate the hemodynamic metrics of the portal venous system, which would promote the more accurate risk stratification of postsplenectomy thrombosis and improve the patient-specific postoperative management.Clinical Relevance- The computational model developed by the present study provides a feasible scheme for simulating postsplenectomy hemodynamic metrics of the portal venous system more accurately, which would benefit the risk prediction and prophylaxis of portal venous system thrombosis for portal hypertensive patients receiving splenectomy.

Pre-Configured Error Pattern Ordered Statistics Decoding for CRC-Polar Codes.

In this paper, we propose a pre-configured error pattern ordered statistics decoding (PEPOSD) algorithm and discuss its application to short cyclic redundancy check (CRC)-polar codes. Unlike the traditional OSD that changes the most reliable independent symbols, we regard the decoding process as testing the error patterns, like guessing random additive noise decoding (GRAND). Also, the pre-configurator referred from ordered reliability bits (ORB) GRAND can better control the range and testing order of EPs. An offline-online structure can accelerate the decoding process. Additionally, we also introduce two orders to optimize the search order for testing EPs. Compared with CRC-aided OSD and list decoding, PEPOSD can achieve a better trade-off between accuracy and complexity.

Genetic characterization of MDR genomic elements carrying two aac(6')-aph(2″) genes in feline-derived clinical Enterococcus faecalis isolate.

Multidrug-resistant Enterococcus faecalis (E. faecalis) often cause intestinal infections in cats. The aim of this study was to investigate a multidrug-resistant E. faecalis isolate for plasmidic and chromosomal antimicrobial resistance and their genetic environment. E. faecalis strain ESC1 was obtained from the feces of a cat. Antimicrobial susceptibility testing was carried out using the broth microdilution method. Conjugation experiments were performed using Escherichia coli and Staphylococcus aureus as receptors. Complete sequences of chromosomal DNA and plasmids were generated by whole genome sequencing (WGS) and bioinformatics analysis for the presence of drug resistance genes and mobile elements. Multidrug-resistant E. faecalis ESC1 contained a chromosome and three plasmids. The amino acid at position 80 of the parC gene on the chromosome was mutated from serine to isoleucine, and hence the amino acid mutation at this site led to the resistance of ESC1 strain to fluoroquinolones. Eleven antibiotic resistance genes were located on two plasmids. We identified a novel composite transposon carrying two aminoglycoside resistance genes aac(6')-aph(2″). This study reported the coexistence of a novel 5.4 kb composite transposon and a resistance plasmid with multiple homologous recombination in an isolate of E. faecalis ESC1. This data provides a basis for understanding the genomic signature and antimicrobial resistance mechanisms of this pathogen.

The Influence of Aortic Valve Disease on Coronary Hemodynamics: A Computational Model-Based Study.

Aortic valve disease (AVD) often coexists with coronary artery disease (CAD), but whether and how the two diseases are correlated remains poorly understood. In this study, a zero-three dimensional (0-3D) multi-scale modeling method was developed to integrate coronary artery hemodynamics, aortic valve dynamics, coronary flow autoregulation mechanism, and systemic hemodynamics into a unique model system, thereby yielding a mathematical tool for quantifying the influences of aortic valve stenosis (AS) and aortic valve regurgitation (AR) on hemodynamics in large coronary arteries. The model was applied to simulate blood flows in six patient-specific left anterior descending coronary arteries (LADs) under various aortic valve conditions (i.e., control (free of AVD), AS, and AR). Obtained results showed that the space-averaged oscillatory shear index (SA-OSI) was significantly higher under the AS condition but lower under the AR condition in comparison with the control condition. Relatively, the overall magnitude of wall shear stress was less affected by AVD. Further data analysis revealed that AS induced the increase in OSI in LADs mainly through its role in augmenting the low-frequency components of coronary flow waveform. These findings imply that AS might increase the risk or progression of CAD by deteriorating the hemodynamic environment in coronary arteries.

Citric Acid Confers Broad Antibiotic Tolerance through Alteration of Bacterial Metabolism and Oxidative Stress.

Antibiotic tolerance has become an increasingly serious crisis that has seriously threatened global public health. However, little is known about the exogenous factors that can trigger the development of antibiotic tolerance, both in vivo and in vitro. Herein, we found that the addition of citric acid, which is used in many fields, obviously weakened the bactericidal activity of antibiotics against various bacterial pathogens. This mechanistic study shows that citric acid activated the glyoxylate cycle by inhibiting ATP production in bacteria, reduced cell respiration levels, and inhibited the bacterial tricarboxylic acid cycle (TCA cycle). In addition, citric acid reduced the oxidative stress ability of bacteria, which led to an imbalance in the bacterial oxidation-antioxidant system. These effects together induced the bacteria to produce antibiotic tolerance. Surprisingly, the addition of succinic acid and xanthine could reverse the antibiotic tolerance induced by citric acid in vitro and in animal infection models. In conclusion, these findings provide new insights into the potential risks of citric acid usage and the relationship between antibiotic tolerance and bacterial metabolism.

Transcriptomic Changes and satP Gene Function Analysis in Pasteurella multocida with Different Levels of Resistance to Enrofloxacin.

Pasteurella multocida (Pm) is one of the major pathogens of bovine respiratory disease (BRD), which can develop drug resistance to many of the commonly used antibiotics. Our earlier research group found that with clinical use of enrofloxacin, Pm was more likely to develop drug resistance to enrofloxacin. In order to better understand the resistance mechanism of Pm to enrofloxacin, we isolated PmS and PmR strains with the same PFGE typing in vitro, and artificially induced PmR to obtain the highly resistant phenotype, PmHR. Then transcriptome sequencing of clinically isolated sensitive strains, resistant and highly drug-resistant strains, treated with enrofloxacin at sub-inhibitory concentrations, were performed. The satP gene, of which the expression changed significantly with the increase in drug resistance, was screened. In order to further confirm the function of this gene, we constructed a satP deletion (ΔPm) strain using suicide vector plasmid pRE112, and constructed the C-Pm strain using pBBR1-MCS, and further analyzed the function of the satP gene. Through a continuously induced resistance test, it was found that the resistance rate of ΔPm was obviously lower than that of Pm in vitro. MDK99, agar diffusion and mutation frequency experiments showed significantly lower tolerance of ΔPm than the wild-type strains. The pathogenicity of ΔPm and Pm was measured by an acute pathogenicity test in mice, and it was found that the pathogenicity of ΔPm was reduced by about 400 times. Therefore, this study found that the satP gene was related to the tolerance and pathogenicity of Pm, and may be used as a target of enrofloxacin synergistic effect.

Gene expression prediction based on neighbour connection neural network utilizing gene interaction graphs.

Having observed that gene expressions have a correlation, the Library of Integrated Network-based Cell-Signature program selects 1000 landmark genes to predict the remaining gene expression value. Further works have improved the prediction result by using deep learning models. However, these models ignore the latent structure of genes, limiting the accuracy of the experimental results. We therefore propose a novel neural network named Neighbour Connection Neural Network(NCNN) to utilize the gene interaction graph information. Comparing to the popular GCN model, our model incorperates the graph information in a better manner. We validate our model under two different settings and show that our model promotes prediction accuracy comparing to the other models.

Computational modeling of electromechanical coupling in human cardiomyocyte applied to study hypertrophic cardiomyopathy and its drug response.

BACKGROUND AND OBJECTIVE: Knowledge of electromechanical coupling in cardiomyocyte and how it is influenced by various pathophysiological factors is fundamental to understanding the pathogenesis of myocardial disease and its response to medication, which is however hard to be thoroughly addressed by clinical/experimental studies due to technical limitations. At this point, computational modeling offers an alternative approach. The main objective of the study was to develop a computational model capable of simulating the process of electromechanical coupling and quantifying the roles of various factors in play in the human left ventricular cardiomyocyte. METHODS: A new electrophysiological model was firstly built by combining several existing electrophysiological models and incorporating the mechanism of electrophysiological homeostasis, which was subsequently coupled to models representing the cross-bridge dynamics and active force generation during excitation-contraction coupling and the passive mechanical properties of cardiomyocyte to yield an integrative electromechanical model. Model parameters were calibrated or optimized based on a large amount of experimental data. The resulting model was applied to delineate the characteristics of electromechanical coupling and explore underlying determinant factors in hypertrophic cardiomyopathy (HCM) cardiomyocyte, as well as quantify their changes in response to different medications. RESULTS: Model predictions captured the major electromechanical characteristics of cardiomyocyte under both normal physiological and HCM conditions. In comparison with normal cardiomyocyte, HCM cardiomyocyte suffered from systemic changes in both electrophysiological and mechanical variables. Numerical simulations of drug response revealed that Mavacamten and Metoprolol could both reduce the active contractility and alleviate calcium overload but had marked differential influences on many other electromechanical variables, which theoretically explained why the two drugs have differential therapeutic effects. In addition, our numerical experiments demonstrated the important role of compensatory ion transport in maintaining electrophysiological homeostasis and regulating cytoplasmic volume. CONCLUSIONS: A sophisticated computational model has the advantage of providing quantitative and integrative insights for understanding the pathogenesis and drug responses of HCM or other myocardial diseases at the level of cardiomyocyte, and hence may contribute as a useful complement to clinical/experimental studies. The model may also be coupled to tissue- or organ-level models to strengthen the physiological implications of macro-scale numerical simulations.

Microfluidics-enabled Serial Assembly of Lipid-siRNA-sorafenib Nanoparticles for Synergetic Hepatocellular Carcinoma Therapy.

Multi-component nanoparticles (mNPs) hold great potential for disease prevention and treatment. However, a major barrier is the lack of versatile platforms to accommodate steps of assembly processes of mNPs. Here the microfluidics-enabled serial assembly (MESA) of mNPs is presented. The microfluidic chip, as a mini-conveyor of initial materials, sequentially enables the assembly of sorafenib supramolecule, electrostatic adsorption of siRNA, and surface assembly of protective lipids. The produced lipid-siRNA-sorafenib nanoparticles (LSS NPs) have ultrahigh encapsulation efficiencies for sorafenib (≈100%) and siRNA (≈95%), which benefit from the accommodation of both fast and slow processes on the chip. Although carrying negative charges, LSS NPs enable cytosolic delivery of agents and high gene silencing efficiency within tumor cells. In vivo, the LSS NPs delivering hypoxia-induced factor (HIF1α)-targeted siRNA efficiently regress tumors of Hep3B xenograft and hepatocellular carcinoma patient-derived primary cells xenograft (PDCX) and finally extend the average survival of PDCX mice to 68 days. Thus, this strategy is promising as a sorafenib/siRNA combination therapy, and MESA can be a universal platform for fabricating complex nanosystems.

PGCLCs of human 45,XO reveal pathogenetic pathways of neurocognitive and psychosocial disorders.

BACKGROUND: Neurocognitive disorders and psychosocial difficulties are common in patients with Turner syndrome and multiple neurodegenerative diseases, yet there is no effective cure. Human primordial germ cells (hPGCs) are pluripotent germline stem cells in early embryo, which pass genetic information from one generation to the next, whereas all somatic cells will die along with the end of life. However, it is not known whether patient hPGCs with Turner syndrome contain information of neurocognitive and psychosocial illness. RESULTS: In this report, we used a high-density of culture system of embryoids derived from iPSCs of a patient with Turner syndrome to ask how pathogenetic pathways are associated with onset of neurocognitive and psychosocial disorders. The hPGC-Like Cells (hPGCLCs) were in vitro specified from iPSCs of 45,XO, 46,XX and 46,XY by the high-density induction of embryoids. Amazingly, we found that the specification process of the hPGCLCs in 45,XO, compared to those in 46,XX and 46,XY, enriched several common pathogenetic pathways regulating neurocognitive and psychosocial disorders, that shared among multiple neurodegenerative diseases and Turner syndrome. The downregulated chemical synaptic transmission pathways, including glutamatergic, GABAergic, and nicotine cholinergic synapses, indicated synaptic dysfunctions, while upregulated pathways that were associated with imbalance of mitochondrial respiratory chain complexes and apoptosis, may contribute to neuronal dysfunctions. Notably, downregulation of three types of ubiquitin ligases E1-E2-E3 and lysosome-associated sulfatases and RAB9A, owing to haploinsufficiency and parental preference of the X chromosome expression, indicated that two pathways of cellular degradation, lysosome and ubiquitin-proteasome, were impaired in the specification process of 45,XO hPGCLCs. This would lead to accumulation of undesired proteins and aggregates, which is a typically pathological hallmark in neurodegenerative diseases. CONCLUSIONS: Our data suggest that the specification process of the hPGCLCs in 45,XO, compared to those in 46,XX and 46,XY, enriched pathogenetic pathways that are associated with the onset of neurocognitive and psychosocial disorders.

Circ_FOXP1 promotes the growth and survival of high glucose-treated human trophoblast cells through the regulation of miR-508-3p/SMAD family member 2 pathway.

Gestational diabetes mellitus (GDM) is a health risk for pregnant women and infants. Emerging evidence suggests that the deregulation of circular RNAs (circRNAs) is associated with the progression of this disorder. The objective of this study was to investigate the role of circ_FOXP1 in GDM. Cell models of GDM were established by treating human trophoblast cells with high glucose (HG). The expression of circ_FOXP1, miR-508-3p and SMAD family member 2 (SMAD2) mRNA was detected by quantitative real-time PCR (qPCR). Cell proliferation was assessed by EdU assay and MTT assay, and cell cycle and cell apoptosis were determined by flow cytometry assay. The protein levels of proliferation- and apoptosis-related markers and SMAD2 were measured by western blot. The relationship between miR-508-3p and circ_FOXP1 or SMAD2 was validated by dual-luciferase reporter assay or pull-down assay. The expression of circ_FOXP1 was downregulated in HG-treated HTR-8/SVneo cells. Circ_FOXP1 overexpression promoted HG-inhibited HTR-8/SVneo cell proliferation and suppressed HG-induced HTR-8/SVneo cell cycle arrest and apoptosis. Circ_FOXP1 positively regulated the expression of SMAD2 by targeting miR-508-3p. MiR-508-3p was overexpressed in HG-treated HTR-8/SVneo cells, and its overexpression reversed the effects of circ_FOXP1 overexpression. MiR-508-3p inhibition also alleviated HG-induced HTR-8/SVneo cell injuries, while the knockdown of SMAD2 abolished these effects. Collectively, circ_FOXP1 promotes the growth and survival of HG-treated human trophoblast cells through the miR-508-3p/SMAD2 pathway, hinting that circ_FOXP1 was involved in GDM progression.

The identification of PSEN1 p.Tyr159Ser mutation in a non-canonic early-onset Alzheimer's disease family.

More than 300 missense mutations in PSEN1 gene have been correlated to the early-onset Alzheimer's disease (EOAD), but given the high diversity of PS1 (the PSEN1 gene product) substrates and the involvement of PS1 in multiple biological functions, different mutants may represent different EOAD etiologies, and how each mutant contributes to the EOAD remains to be further investigated. Here we report the identification of a novel PSEN1 p.Tyr159Ser in a family with multiple EOAD cases. The mutant PS1 protein (PS1Y159S) was analyzed for its activity in producing amyloid-ß (Aß) and for the efficiency in maturation in vitro. We also screened other mutations and SNPs that may modify the effect of PSEN1 p.Tyr159Ser on AD pathogenesis. The blood samples of the family were collected for whole-exome gene sequencing and analysis. The identified mutant PS1 and several other PS1 mutants were co-expressed with the APP Swedish mutant to compare the effects on APP processing and PS1 maturation.1. The proband and her siblings over 50 years old showed typical AD or MCI symptoms. Exon sequencing identified the p.Tyr159Ser mutation in the PSEN1 gene. As not until the age of 78 did the proband's mother who carried this mutation displayed the symptoms of uncharacterized neuropsychiatry instead of AD, but all the mutation bearing lower generation developed AD or MCI after the age of 50, we also analyzed mutations/SNPs that are different between the mother and the lower generation. By in vitro assays, we found that the Y159S substitution strongly increased Aß42/Aß40 ratio and significantly affected PS1 maturation. The newly discovered PSEN1 p.Tyr159Ser is an AD-causing mutation, yet, the carriers are not obligated AD patients. Mutations/SNPs in other gene may modify the effects of this mutation, and the identification of these mutations/SNPs may facilitate the discovery of AD-preventing mechanisms and methods.

An In Vivo Data-Based Computational Study on Sitting-Induced Hemodynamic Changes in the External Iliac Artery.

Although sedentary behavior (characterized by prolonged sitting without otherwise being active in daily life) is widely regarded as a risk factor for peripheral artery disease (PAD), underlying biomechanical mechanisms remain insufficiently understood. In this study, geometrical models of ten external iliac arteries were reconstructed based on angiographic data acquired from five healthy young subjects resting in supine and sitting (mimicked by side lying with bent legs) positions, respectively, which were further combined with measured blood flow velocity waveforms in the common iliac arteries (with each body posture being maintained for 30 min) to build computational models for simulating intra-arterial hemodynamics. Morphological analyses showed that the external iliac arteries suffered from evident bending deformation upon the switch of body posture from supine to sitting. Measured blood flow velocity waveforms in the sitting position exhibited a marked decrease in mean flow velocity while increase in retrograde flow ratio compared with those in the supine position. Hemodynamic computations further revealed that sitting significantly altered blood flow patterns in the external iliac arteries, leading to a marked enlargement of atheroprone wall regions exposed to low and oscillatory wall shear stress (WSS), and enhanced multidirectional disturbance of WSS that may further impair endothelial function. In summary, our study demonstrates that prolonged sitting induces atheropromoting hemodynamic changes in the external iliac artery due to the combined effects of vascular bending deformation and changes in flow velocity waveform, which may provide important insights for understanding the involvement of biomechanical factors in sedentary behavior-related PAD.

Biodegradable freestanding rare-earth nanosheets promote multimodal imaging and delivers CRISPR-Cas9 plasmid against tumor.

Designing nanomaterials for bio-imaging and drug delivery for advanced cancer therapy with biodegradability and biocompatibility is a promising but challenging frontier. Herein, we assembled biodegradable and biocompatible ultrathin rare-earth erbium/dysprosium nanosheets that improve contrast in multimodal bio-imaging settings (MRI and X-ray CT) and deliver CRISPR-Cas9 plasmid to treat tumors.

Combined Support Vector Machine Classifier and Brain Structural Network Features for the Individual Classification of Amnestic Mild Cognitive Impairment and Subjective Cognitive Decline Patients.

OBJECTIVE: Individuals with subjective cognitive decline (SCD) or amnestic mild cognitive impairment (aMCI) represent important targets for the early detection and intervention of Alzheimer's disease (AD). In this study, we employed a multi-kernel support vector machine (SVM) to examine whether white matter (WM) structural networks can be used for screening SCD and aMCI. METHODS: A total of 138 right-handed participants [51 normal controls (NC), 36 SCD, 51 aMCI] underwent MRI brain scans. For each participant, three types of WM networks with different edge weights were constructed with diffusion MRI data: fiber number-weighted networks, mean fractional anisotropy-weighted networks, and mean diffusivity (MD)-weighted networks. By employing a multiple-kernel SVM, we seek to integrate information from three weighted networks to improve classification performance. The accuracy of classification between each pair of groups was evaluated via leave-one-out cross-validation. RESULTS: For the discrimination between SCD and NC, an area under the curve (AUC) value of 0.89 was obtained, with an accuracy of 83.9%. Further analysis revealed that the methods using three types of WM networks outperformed other methods using single WM network. Moreover, we found that most of discriminative features were from MD-weighted networks, which distributed among frontal lobes. Similar classification performance was also reported in the differentiation between subjects with aMCI and NCs (accuracy = 83.3%). Between SCD and aMCI, an AUC value of 0.72 was obtained, with an accuracy of 72.4%, sensitivity of 74.5% and specificity of 69.4%. The highest accuracy was achieved with features only selected from MD-weighted networks. CONCLUSION: White matter structural network features help machine learning algorithms accurately identify individuals with SCD and aMCI from NCs. Our findings have significant implications for the development of potential brain imaging markers for the early detection of AD.

Integrated Microfluidic Synthesis of Aptamer Functionalized Biozeolitic Imidazolate Framework (BioZIF-8) Targeting Lymph Node and Tumor.

Targeted delivery of therapeutic molecules using nanomaterials is desired to elicit specific responses toward diseases. Such an integrated synthesis of functional material using a microfluidic approach is a great challenge. Functional metal organic frameworks (MOFs) with unique structural diversity possess a complicated synthesis procedure thereby requiring a modest, straightforward approach to synthesize size-controllable MOFs. Here, we develop an integrated microfluidic chip to synthesize the aptamer-modified biozeolitic imidazolate framework (BioZIF-8) to target the lymph node and tumor. The first stage of the microfluidic chip forms the ZIF-8 encapsulating biomolecules (bovine serum albumin, small interfering ribonucleic acid, and doxorubicin). The second stage modifies the surface of BioZIF-8 with the aptamer. Our approach reduces the overall synthesis time (∼3 mg/10 min against 15 h for the conventional two-step method) and encapsulates a higher number of biomolecules. The microfluidic approach realizes the rapid and fine-tuned synthesis of functional MOFs integrated into one-step.

Bright Aggregation-Induced Emission Nanoparticles for Two-Photon Imaging and Localized Compound Therapy of Cancers.

Photodynamic therapy (PDT), a noninvasive therapeutic strategy for cancer treatment, which always suffers from the low reactive oxygen species (ROS) yield of traditional organic dyes. Herein, we present lipid-encapsulated aggregation-induced emission nanoparticles (AIE NPs) that have a high quantum yield (23%) and a maximum two-photon absorption (TPA) cross-section of 560 GM irradiated by near-infrared light (800 nm). The AIE NPs can serve as imaging agents for spatiotemporal imaging of tumor tissues with a penetration depth up to 505 µm on mice melanoma model. Importantly, the AIE NPs can simultaneously generate singlet oxygen (1O2) and highly toxic hydroxyl radicals (•OH) upon irradiation with 800 nm irradiation for photodynamic tumor ablation. In addition, the AIE NPs can be effectively cleared from the mouse body after the imaging and therapy. This study provides a strategy to develop theranostic agents for cancer image-guided PDT with high brightness, superior photostability, and high biosafety.