A Red-Emission Fluorescent Probe with Large Stokes Shift for Detection of Viscosity in Living Cells and Tumor-Bearing Mice.

Abnormal viscosity is closely related to the occurrence of many diseases, such as cancer. Therefore, real-time detection of changes in viscosity in living cells is of great importance. Fluorescent molecular rotors play a critical role in detecting changes in cellular viscosity. Developing red emission viscosity probes with large Stokes shifts and high sensitivity and specificity remains an urgent and important topic. Herein, a novel viscosity-sensitive fluorescent probe (TCF-VIS1) with a large stokes shift and red emission was prepared based on the 2-dicyanomethylene-3-cyano-4,5,5-trimethyl-2,5-dihydrofuran (TCF) skeleton. Due to intramolecular rotation, the probe itself does not fluorescence at low viscosity. With the increase in viscosity, the rotation of TCF-VIS1 is limited, and its fluorescence is obviously enhanced. The probe has the advantages of simple preparation, large Stokes shift, good sensitivity and selectivity, and low cytotoxicity, which make it successfully used for viscosity detection in living cells. Moreover, TCF-VIS1 showed its potential for cancer diagnosis at the cell level and in tumor-bearing mice by detecting viscosity. Therefore, the probe is expected to enrich strategies for the detection of viscosity in biological systems and offer a potential tool for cancer diagnosis.

Self-Feedback DNAzyme Motor for Cascade-Amplified Imaging of mRNA in Live Cells and In Vivo.

DNA motors have attracted extensive interest in biosensing and bioimaging. However, the amplification capacity of the existing DNA motor systems is limited since the products from the walking process are unable to feedback into the original DNA motor systems. As a result, the sensitivities of such systems are limited in the contexts of biosensing and bioimaging. In this study, we report a novel self-feedback DNAzyme motor for the sensitive imaging of tumor-related mRNA in live cells and in vivo with cascade signal amplification capacity. Gold nanoparticles (AuNPs) are modified with hairpin-locked DNAzyme walker and track strands formed by hybridizing Cy5-labeled DNA trigger-incorporated substrate strands with assistant strands. Hybridization of the target mRNA with the hairpin strands activates DNAzyme and promotes the autonomous walking of DNAzyme on AuNPs through DNAzyme-catalyzed substrate cleavage, resulting in the release of many Cy5-labeled substrate segments containing DNA triggers and the generation of an amplified fluorescence signal. Moreover, each released DNA trigger can also bind with the hairpin strand to activate and operate the original motor system, which induces further signal amplification via a feedback mechanism. This motor exhibits a 102-fold improvement in detection sensitivity over conventional DNAzyme motors and high selectivity for target mRNA. It has been successfully applied to distinguish cancer cells from normal cells and diagnose tumors in vivo based on mRNA imaging. The proposed DNAzyme motor provides a promising paradigm for the amplified detection and sensitive imaging of low-abundance biomolecules in vivo.

FSMN-Net: a free space matching network based on manifold convolution for optical molecular tomography.

Optical molecular tomography (OMT) can monitor glioblastomas in small animals non-invasively. Although deep learning (DL) methods have made remarkable achievements in this field, improving its generalization against diverse reconstruction systems remains a formidable challenge. In this Letter, a free space matching network (FSMN-Net) was presented to overcome the parameter mismatch problem in different reconstruction systems. Specifically, a novel, to the best of our knowledge, manifold convolution operator was designed by considering the mathematical model of OMT as a space matching process. Based on the dynamic domain expansion concept, an end-to-end fully convolutional codec further integrates this operator to realize robust reconstruction with voxel-level accuracy. The results of numerical simulations and in vivo experiments demonstrate that the FSMN-Net can stably generate high-resolution reconstruction volumetric images under different reconstruction systems.

Dynamic fluorescence molecular tomography metabolic parameters solution based on problem decomposition and prior refactor.

Dynamic fluorescence molecular tomography (DFMT), as a noninvasive optical imaging method, can quantify metabolic parameters of living animal organs and assist in the diagnosis of metabolic diseases. However, existing DFMT methods do not have a high capacity to reconstruct abnormal metabolic regions, and require additional prior information and complicated solution methods. This paper introduces a problem decomposition and prior refactor (PDPR) method. The PDPR decomposes the metabolic parameters into two kinds of problems depending on their temporal coupling, which are solved using regularization and parameter fitting. Moreover, PDPR introduces the idea of divide-and-conquer to refactor prior information to ensure discrimination between metabolic abnormal regions and normal tissues. Experimental results show that PDPR is capable of separating abnormal metabolic regions of the liver and has the potential to quantify metabolic parameters and diagnose liver metabolic diseases in small animals.

Structure-fused deep 3D hierarchical network: A bioluminescence tomography scheme for different imaging objects.

Monte Carlo eXtreme (MCX) method has a unique advantage for deep neural network based bioluminescence tomography (BLT) reconstruction. However, this method ignores the distribution of sources energy and relies on the determined tissue structure. In this paper, a deep 3D hierarchical reconstruction network for BLT was proposed where the inputs were divided into two parts -- bioluminescence image (BLI) and anatomy of the imaged object by CT. Firstly, a parallel encoder is used to feature the original BLI & CT slices and integrate their features to distinguish the different tissue structure of imaging objects; Secondly, GRU is used to fit the spatial information of different slices and convert it into 3D features; Finally, the 3D features are decoded to the spacial and energy information of source by a symmetrical decoding structure. Our research suggested that this method can effectively compute the radiation intensity and the spatial distribution of the source for different imaging object.

One-dimensional convolutional neural network for Jacobian in Diffuse Optical Tomography.

Non-linear least square minimization algorithms are often employed to solve Diffuse Optical Tomography (DOT) inverse problem. However, it is time-consuming to calculate the Jacobian matrix. This work has proposed a data-driven neural network method to improve computational efficiency. The singular value decomposition is employed to compute the updated Jacobian and a mapping from boundary measurements to the singular values based on a convolutional neural network (CNN) is learned to obtain the singular values. The method is validated with 3D numerical simulation data. We have demonstrated that the approach can save computation time compared to Adjoint method, and reconstructed absorption coefficient close to Adjoint method.Clinical Relevance- These results are not focused on clinical relevance currently, but in the future may be helpful to accelerant DOT reconstruction in clinic.

Discovery of dihydropyridinone derivative as a covalent EZH2 degrader.

EZH2 is overexpressed in multiple types of cancer and high expression level of EZH2 correlates with poor prognosis. Besides the regulation of H3K27 trimethylation, EZH2 itself regulates its downstream proteins in a PRC2- and methylation-independent way. Starting from an approved EZH2 inhibitor EPZ-6438, we used covalent drug design and medicinal chemistry approaches to discover a novel covalent EZH2 degrader 38, which forms a covalent bond with EZH2 Cys663 and showed strong biochemical activities against EZH2 WT and mutants. Compound 38 exhibited potent antiproliferation effects against both B-cell lymphoma and TNBC cell lines by reducing the levels of H3K27me3 and EZH2. The mass spectrometry, washout and competition experiments confirmed the covalent binding of 38 to EZH2. This study demonstrates that covalent EZH2 degraders could provide an opportunity for the development of promising new drug candidates.

Liver injury monitoring using dynamic fluorescence molecular tomography based on a time-energy difference strategy.

Dynamic fluorescence molecular tomography (DFMT) is a promising molecular imaging technique that offers the potential to monitor fast kinetic behaviors within small animals in three dimensions. Early monitoring of liver disease requires the ability to distinguish and analyze normal and injured liver tissues. However, the inherent ill-posed nature of the problem and energy signal interference between the normal and injured liver regions limit the practical application of liver injury monitoring. In this study, we propose a novel strategy based on time and energy, leveraging the temporal correlation in fluorescence molecular imaging (FMI) sequences and the metabolic differences between normal and injured liver tissue. Additionally, considering fluorescence signal distribution disparity between the injured and normal regions, we designed a universal Golden Ratio Primal-Dual Algorithm (GRPDA) to reconstruct both the normal and injured liver regions. Numerical simulation and in vivo experiment results demonstrate that the proposed strategy can effectively avoid signal interference between liver and liver injury energy and lead to significant improvements in morphology recovery and positioning accuracy compared to existing approaches. Our research presents a new perspective on distinguishing normal and injured liver tissues for early liver injury monitoring.

Engineering of dendritic cell bispecific extracellular vesicles for tumor-targeting immunotherapy.

Advances in the development of therapeutic extracellular vesicles (EVs) for cancer immunotherapy have allowed them to emerge as an alternative to cell therapy. In this proof-of-concept work, we develop bispecific EVs (BsEVs) by genetically engineering EV-producing dendritic cells (DCs) with aCD19 scFv and PD1 for targeting tumor antigens and blocking immune checkpoint proteins simultaneously. We find that these bispecific EVs (EVs-PD1-aCD19) have an impressive ability to accumulate in huCD19-expressing solid tumors following intravenous injection. In addition, EVs-PD1-aCD19 can remarkably reverse the immune landscape of the solid tumor by blocking PD-L1. Furthermore, EVs-PD1-aCD19 can also target tumor-derived EVs in circulation, which prevents the formation of a premetastatic niche in other tissues. Our technology is a demonstration of bispecific EV-based cancer immunotherapy, which may inspire treatments against various types of tumors with different surface antigens and even a patient-tailored therapy.

Predicting and overcoming resistance to CDK9 inhibitors for cancer therapy.

Abnormally activated CDK9 participates in the super-enhancer mediated transcription of short-lived proteins required for cancer cell survival. Targeting CDK9 has shown potent anti-tumor activity in clinical trials among different cancers. However, the study and knowledge on drug resistance to CDK9 inhibitors are very limited. In this study, we established an AML cell line with acquired resistance to a highly selective CDK9 inhibitor BAY1251152. Through genomic sequencing, we identified in the kinase domain of CDK9 a mutation L156F, which is also a coding SNP in the CDK9 gene. By knocking in L156F into cancer cells using CRISPR/Cas9, we found that single CDK9 L156F could drive the resistance to CDK9 inhibitors, not only ATP competitive inhibitor but also PROTAC degrader. Mechanistically, CDK9 L156F disrupts the binding with inhibitors due to steric hindrance, further, the mutation affects the thermal stability and catalytic activity of CDK9 protein. To overcome the drug resistance mediated by the CDK9-L156F mutation, we discovered a compound, IHMT-CDK9-36 which showed potent inhibition activity both for CDK9 WT and L156F mutant. Together, we report a novel resistance mechanism for CDK9 inhibitors and provide a novel chemical scaffold for the future development of CDK9 inhibitors.

Discovery of Pyrazolo[1,5-a]pyrimidine derivative as a potent and selective PI3Kγ/δ dual inhibitor.

Phosphoinositol 3-kinases (PI3Ks) γ and δ are primarily expressed in leukocytes and play crucial roles in regulation of the immune system. Dual inhibition of PI3Kγ/δ has emerged as an effective approach to regulate the tumor microenvironment. Here, we report the exploration of structure-activity relationship optimization which led to the discovery of a potent PI3Kγ/δ dual inhibitor 15u (IHMT-PI3K-455). 15u exhibits strong potency in biochemical and cellular assays and it repolarizes M2 phenotype toward M1 phenotype in THP-1 and BMDM macrophages. In addition, it shows suitable in vivo properties as demonstrated through pharmacokinetic studies in rats and pharmacodynamics properties in a MC38 xenograft model.

Brain-targeted nanoreactors prevent the development of organophosphate-induced delayed neurological damage.

BACKGROUND: Organophosphate (OP)-induced delayed neurological damage is attributed to permanent neuropathological lesions caused by irreversible OP-neurocyte interactions, without potent brain-targeted etiological antidotes to date. The development of alternative therapies to achieve intracerebral OP detoxification is urgently needed. METHODS: We designed a brain-targeted nanoreactor by integrating enzyme immobilization and biomimetic membrane camouflaging protocols with careful characterization, and then examined its blood-brain barrier (BBB) permeability both in vitro and in vivo. Subsequently, the oxidative stress parameters, neuroinflammatory factors, apoptotic proteins and histopathological changes were measured and neurobehavioral tests were performed. RESULTS: The well-characterized nanoreactors exerted favourable BBB penetration capability both in vitro and in vivo, significantly inhibiting OP-induced intracerebral damage. At the cellular and tissue levels, nanoreactors obviously blocked oxidative stress, cellular apoptosis, inflammatory reactions and brain histopathological damage. Furthermore, nanoreactors radically prevented the occurrence of OP-induced delayed cognitive deficits and psychiatric abnormality. CONCLUSION: The nanoreactors significantly prevented the development of OP-induced delayed neurological damage, suggesting a potential brain-targeted etiological strategy to attenuate OP-related delayed neurological and neurobehavioral disorders.

A novel staging system derived from natural language processing of pathology reports to predict prognostic outcomes of pancreatic cancer: a retrospective cohort study.

OBJECTIVE: To construct a novel tumor-node-morphology (TNMor) staging system derived from natural language processing (NLP) of pathology reports to predict outcomes of pancreatic ductal adenocarcinoma. METHOD: This retrospective study with 1657 participants was based on a large referral center and The Cancer Genome Atlas Program (TCGA) dataset. In the training cohort, NLP was used to extract and screen prognostic predictors from pathology reports to develop the TNMor system, which was further evaluated with the tumor-node-metastasis (TNM) system in the internal and external validation cohort, respectively. Main outcomes were evaluated by the log-rank test of Kaplan-Meier curves, the concordance index (C-index), and the area under the receiver operating curve (AUC). RESULTS: The precision, recall, and F1 scores of the NLP model were 88.83, 89.89, and 89.21%, respectively. In Kaplan-Meier analysis, survival differences between stages in the TNMor system were more significant than that in the TNM system. In addition, our system provided an improved C-index (internal validation, 0.58 vs. 0.54, P <0.001; external validation, 0.64 vs. 0.63, P <0.001), and higher AUCs for 1, 2, and 3-year survival (internal validation: 0.62 vs. 0.54, P <0.001; 0.64 vs. 0.60, P= 0.017; 0.69 vs. 0.62, P= 0.001; external validation: 0.69 vs. 0.65, P= 0.098; 0.68 vs. 0.64, P= 0.154; 0.64 vs. 0.55, P= 0.032, respectively). Finally, our system was particularly beneficial for precise stratification of patients receiving adjuvant therapy, with an improved C-index (0.61 vs. 0.57, P <0.001), and higher AUCs for 1-year, 2-year, and 3-year survival (0.64 vs. 0.57, P <0.001; 0.64 vs. 0.58, P <0.001; 0.67 vs. 0.61, P <0.001; respectively) compared with the TNM system. CONCLUSION: These findings suggest that the TNMor system performed better than the TNM system in predicting pancreatic ductal adenocarcinoma prognosis. It is a promising system to screen risk-adjusted strategies for precision medicine.

A S-substituted Nile Blue-derived bifunctional near-infrared fluorescent probe for in vivo carboxylesterase imaging-guided photodynamic therapy of hepatocellular carcinoma.

The development of theranostic probes that integrate both diagnostic and therapeutic functions still remains an intractable challenge in precise cancer treatment. Herein, a novel bifunctional near-infrared (NIR) fluorescent probe (CEP1) for carboxylesterase (CE) imaging and photodynamic therapy (PDT) of hepatocellular carcinoma (HCC) has been firstly developed and successfully applied in vitro and in vivo. The probe was constructed by introducing carbamate as both the recognition unit and the fluorescence quenching unit into the fluorophore S-substituted Nile Blue (ENBS) via a self-eliminating spacer with substituted chloride. It can be activated by CE and hydrolyzed into fluorescent ENBS, which recover fluorescence at about 700 nm, and can generate superoxide radical anions under NIR irradiation. Additionally, the probe could effectively distinguish tumor cells from normal cells by CE imaging of live cells. Furthermore, it could achieve CE imaging in vivo and significantly inhibits tumor growth by imaging-guided PDT. Therefore, this study offers a promising and attractive platform for activatable imaging-guided PDT of HCC.

3D-deep optical learning: a multimodal and multitask reconstruction framework for optical molecular tomography.

Optical molecular tomography (OMT) is an emerging imaging technique. To date, the poor universality of reconstruction algorithms based on deep learning for various imaged objects and optical probes limits the development and application of OMT. In this study, based on a new mapping representation, a multimodal and multitask reconstruction framework-3D deep optical learning (3DOL), was presented to overcome the limitations of OMT in universality by decomposing it into two tasks, optical field recovery and luminous source reconstruction. Specifically, slices of the original anatomy (provided by computed tomography) and boundary optical measurement of imaged objects serve as inputs of a recurrent convolutional neural network encoded parallel to extract multimodal features, and 2D information from a few axial planes within the samples is explicitly incorporated, which enables 3DOL to recognize different imaged objects. Subsequently, the optical field is recovered under the constraint of the object geometry, and then the luminous source is segmented by a learnable Laplace operator from the recovered optical field, which obtains stable and high-quality reconstruction results with extremely few parameters. This strategy enable 3DOL to better understand the relationship between the boundary optical measurement, optical field, and luminous source to improve 3DOL's ability to work in a wide range of spectra. The results of numerical simulations, physical phantoms, and in vivo experiments demonstrate that 3DOL is a compatible deep-learning approach to tomographic imaging diverse objects. Moreover, the fully trained 3DOL under specific wavelengths can be generalized to other spectra in the 620-900 nm NIR-I window.

Discovery of a highly potent pan-RAF inhibitor IHMT-RAF-128 for cancer treatment.

Although rat sarcoma viral oncogene homolog (RAS) mutations occur in about 30% of solid tumors, targeting RAS mutations other than KRAS-G12C is still challenging. As an alternative approach, developing inhibitors targeting RAF, the downstream effector of RAS signaling, is currently one of the main strategies for cancer therapy. Selective v-raf murine sarcoma viral oncogene homolog B1 (BRAF)-V600E inhibitors Vemurafenib, Encorafenib, and Dabrafenib have been approved by FDA and received remarkable clinical responses, but these drugs are ineffective against RAS mutant tumors due to limited inhibition on dimerized RAF. In this study, we developed a highly potent pan-RAF inhibitor, IHMT-RAF-128, which exhibited similarly high efficacies in inhibiting both partners of the RAF dimer, and showed potent anti-tumor efficacy against a variety of cancer cells harboring either RAF or RAS mutations, especially Adagrasib and Sotorasib (AMG510) resistant-KRAS-G12C secondary mutations, such as KRAS-G12C-Y96C and KRAS-G12C-H95Q. In addition, IHMT-RAF-128 showed excellent pharmacokinetic profile (PK), and the bioavailability in mice and rats were 63.9%, and 144.1%, respectively. Furthermore, IHMT-RAF-128 exhibited potent anti-tumor efficacy on xenograft mouse tumor models in a dose-dependent manner without any obvious toxicities. Together, these results support further investigation of IHMT-RAF-128 as a potential clinical drug candidate for the treatment of cancer patients with RAF or RAS mutations.

Structure-based discovery of IHMT-IDH1-053 as a potent irreversible IDH1 mutant selective inhibitor.

Through a structure-based irreversible drug design approach, we have discovered a highly potent IDH1-mutant inhibitor compound 16 (IHMT-IDH1-053) (IC50 = 4.7 nM), which displays high selectivity against IDH1 mutants over IDH1 wt and IDH2 wt/mutants. The crystal structure demonstrates that 16 binds to the IDH1 R132H protein in the allosteric pocket adjacent to the NAPDH binding pocket through a covalent bond with residue Cys269. 16 inhibits 2-hydroxyglutarate (2-HG) production in IDH1 R132H mutant transfected 293T cells (IC50 = 28 nM). In addition, it inhibits the proliferation of HT1080 cell line and primary AML cells which both bear IDH1 R132 mutants. In vivo, 16 inhibits 2-HG level in a HT1080 xenograft mouse model. Our study suggested that 16 would be a new pharmacological tool to study IDH1 mutant-related pathology and the covalent binding mode provided a novel approach for designing irreversible IDH1 inhibitors.

An adaptive parameter selection strategy based on maximizing the probability of data for robust fluorescence molecular tomography reconstruction.

To alleviate the ill-posed of the inverse problem in fluorescent molecular tomography (FMT), many regularization methods based on L2 or L1 norm have been proposed. Whereas, the quality of regularization parameters affects the performance of the reconstruction algorithm. Some classical parameter selection strategies usually need initialization of parameter range and high computing costs, which is not universal in the practical application of FMT. In this paper, an universally applicable adaptive parameter selection method based on maximizing the probability of data (MPD) strategy was proposed. This strategy used maximum a posteriori (MAP) estimation and maximum likelihood (ML) estimation to establish a regularization parameters model. The stable optimal regularization parameters can be determined by multiple iterative estimates. Numerical simulations and in vivo experiments show that MPD strategy can obtain stable regularization parameters for both regularization algorithms based on L2 or L1 norm and achieve good reconstruction performance.

Nanoplastics Shape Adaptive Anticancer Immunity in the Colon in Mice.

The impact of nanoplastics (NPs) on human health is still not well understood, and more research is needed to better understand the risks associated with these particles. In this study, we found that oral administration of polyethylene (PE) NPs in a mice model significantly disrupted the intestinal microenvironment, which shapes adaptive immune response and favors the established in situ colorectal tumor growth. Using single-cell RNA sequencing technology, we show that NPs triggered colon IL-1ß-producing macrophages by inducing lysosome damage, leading to colonic Treg and Th17 differentiation associated with T cell exhaustion, which creates a colon environment that favors the tumor initiation and progress. A similar effect is also observed in polystyrene NPs. Our result provides insight into the potential link between NPs ingestion and colon tumorigenesis, and the urgency of addressing plastic pollution worldwide.