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Abstract Global mortality data indicates cancer as the second-leading cause of death worldwide. Therefore, there’s a pressing need to innovate effective treatments to address this significant medical and societal challenge. In recent years, X-ray-induced photodynamic therapy (X-PDT) has emerged as a promising advancement, revolutionizing traditional photodynamic therapy (PDT) for deeply entrenched malignancies by harnessing penetrating X-rays as external stimuli. Recent developments in X-ray photodynamic therapy have shown a trend toward minimizing radiation doses to remarkably low levels after the proof-of-concept demonstration. Early detection and real-time monitoring are crucial aspects of effective cancer treatment. Sophisticated X-ray imaging techniques have been enhanced by the introduction of X-ray luminescence nano-agents, alongside contrast nanomaterials based on X-ray attenuation. X-ray luminescence-based in vivo imaging offers excellent detection sensitivity and superior image quality in deep tissues at a reasonable cost, due to unhindered penetration and unimpeded auto-fluorescence of X-rays. This review emphasizes the significance of X-ray responsive theranostics, exploring their mechanism of action, feasibility, biocompatibility, and promising prospects in imaging-guided therapy for deep-seated tumors. Additionally, it discusses promising applications of X-PDT in treating breast cancer, liver cancer, lung cancer, skin cancer, and colorectal cancer. Graphical Abstract

Abstract Existing strategies use bifunctional chimaeras to mediate extracellular protein degradation. However, these strategies rely on specific lysosome-trafficking receptors to facilitate lysosomal delivery, which may raise resistance concerns due to intrinsic cell-to-cell variation in receptor expression and mutations or downregulation of the receptors. Another challenge is establishing a universal platform applicable in multiple scenarios. Here, we develop MONOTAB (MOdified NanOparticle with TArgeting Binders), a plug-and-play monofunctional degradation platform that can drag extracellular targets into lysosomes for degradation. MONOTAB harnesses the inherent lysosome-targeting ability of certain nanoparticles to obviate specific receptor dependency and the hook effect. To achieve high modularity and programmable target specificity, we utilize the streptavidin-biotin interaction to immobilize antibodies or other targeting molecules on nanoparticles, through an antibody mounting approach or by direct binding. Our study reveals that MONOTAB can induce efficient degradation of diverse therapeutic targets, including membrane proteins, secreted proteins, and even extracellular vesicles.

To address the problems of strain localization, the exact Mohr-Coulomb (MC) model is used based on second-order cone programming (mpcFEM-SOCP) in the framework of micropolar continuum finite element method. Using the uniaxial compression test, we focused on the earth pressure problem of rigid wall segment involving non-associated plasticity. The numerical results reveal that when mpcFEM-SOCP is applied, the problems of mesh dependency can be effectively addressed. For geotechnical strain localization analysis involving non-associated MC plasticity, mpcFEM-SOCP in conjunction with the pseudo-time discrete scheme can improve the numerical stability and avoid the unreasonable softening issue in the pressure-displacement curves, which may be encountered in the conventional FEM. It also shows that the pressure-displacement responses calculated by mpcFEM-SOCP with the pseudo-time discrete scheme are higher than those calculated by mpcFEM-SOCP with the Davis scheme. The inclination angle of shear band predicted by mpcFEM-SOCP with the pseudo-time discrete scheme agrees well with the theoretical solution of non-associated MC plasticity.

Abstract Chemo‐immunotherapy has made significant progress in cancer treatment. However, the cancer cell self‐defense mechanisms, including cell cycle checkpoint and programmed cell death‐ligand 1 (PD‐L1) upregulation, have greatly hindered the therapeutic efficacy. Herein, norcantharidin (NCTD)‐platinum (Pt) codelivery nanoparticles (NC‐NP) with tumor‐sensitive release profiles are designed to overcome the self‐defense mechanisms via synergistic chemo‐immunotherapy. NC‐NP remains stable under normal physiological conditions but quickly releases 1,2‐diaminocyclohexane‐platinum(II) (DACHPt, a parent drug of oxaliplatin) and NCTD in response to the tumor acidity. NCTD inhibits protein phosphatase 2A (PP2A) activity to relieve cell cycle arrest and downregulates the tumor PD‐L1 expression to disrupt the programmed cell death‐1 (PD‐1)/PD‐L1 interaction, synergistically enhancing Pt‐based chemotherapy and immunogenic cell death‐induced immunotherapy. As a result, NC‐NP exhibits potent synergistic cytotoxicity and promotes T cell recruitment to generate robust antitumor immune responses. The dual synergism exhibits potent antitumor activity against orthotopic 4T1 tumors, providing a promising chemo‐immunotherapy paradigm for cancer treatment.

Cell transplantation has been proved the promising therapeutic effects on intervertebral disc degeneration (IVDD). However, the increased levels of reactive oxygen species (ROS) in the degenerated region will impede the efficiency of human adipose-derived stem cells (human ADSCs) transplantation therapy. It inhibits human ADSCs proliferation, and increases human ADSCs apoptosis. Herein, we firstly devised a novel amphiphilic copolymer PEG-PAPO, which could self-assemble into a nanosized micelle and load lipophilic kartogenin (KGN), as a single complex (PAKM). It was an injectable esterase-responsive micelle, and showed controlled release ability of KGN and apocynin (APO). Oxidative stimulation promoted the esterase activity in human ADSCs, which accelerate degradation of esterase-responsive micelle. Compared its monomer, the PAKM micelle possessed better bioactivities, which were attributed to their synergistic effect. It enhanced the viability, autophagic activation (P62, LC3 II), ECM-related transcription factor (SOX9), and ECM (Collagen II, Aggrecan) maintenance in human ADSCs. Furthermore, it is demonstrated that the injection of PAKM with human ADSCs yielded higher disc height and water content in rats. Therefore, PAKM micelles perform promoting cell survival and differentiation effects, and may be a potential therapeutic agent for IVDD.

Some chemotherapeutic drugs, such as doxorubicin, induce immunogenic cell death (ICD) and promote anti-tumor immune responses. Here the authors report that the histone demethylase inhibitor 5-carboxy-8-hydroxyquinoline (IOX1) reduces the expression of PD-L1 in cancer cells and enhances doxorubicin-induced ICD, promoting T cell infiltration and reducing tumor growth in preclinical models.

Abstract The cancer drug delivery process involves a series of biological barriers, which require the nanomedicine to exhibit different, even opposite properties for high therapeutic efficacy. The prevailing design philosophy, i.e., integrating these properties within one nanomedicine via on‐demand property transitions such as PEGylation/dePEGylation, complicates nanomedicines’ composition and thus impedes clinical translation. Here, polyzwitterionic micelles of poly(tertiary amine‐oxide)‐block‐poly(ε‐caprolactone) (PTAO‐PCL) amphiphiles that enable all the required functions are presented. The zwitterionic nature and unique cell membrane affinity confer the PTAO micelles long blood circulation, efficient tumor accumulation and penetration, and fast cellular internalization. The mitochondrial targeting capability allows drug delivery into the mitochondria to induce mitochondrial dysfunction and overcome tumor multidrug resistance. As a result, the PTAO/drug micelles exhibit potent anticancer efficacy. This simple yet multipotent carrier system holds great promise as a generic platform for potential clinical translation.

Immune checkpoint blockade (ICB) therapies that target programmed cell death-1 (PD-1)/programmed cell death-ligand 1 (PD-L1) pathway are currently used for the treatment of various cancer types. However, low response rates of ICB remain the major issue and limit their applications in clinic. Here, we developed a ROS-responsive synergistic delivery system (pep-PAPM@PTX) by integrating physically-encapsulated paclitaxel (PTX) and surface-modified anti-PD-L1 peptide (pep) for combined chemotherapy and ICB therapy. Pep-PAPM@PTX could bind the cell surface PD-L1 and drive its recycling to lysosomal degradation, thus reverting PTX-induced PD-L1 upregulation and downregulating PD-L1 expression. As a result, pep-PAPM@PTX significantly promoted T cell infiltration and increased tumor immunoactivating factors, synergizing PTX chemotherapy to achieve enhanced anticancer potency in a triple-negative breast cancer (TNBC) model.

Background: Assistive automatic seizure detection can empower human annotators to shorten patient monitoring data review times. We present a proof-of-concept for a seizure detection system that is sensitive, automated, patient-specific, and tunable to maximise sensitivity while minimizing human annotation times. The system uses custom data preparation methods, deep learning analytics and electroencephalography (EEG) data. Methods: Scalp EEG data of 365 patients containing 171,745 s ictal and 2,185,864 s interictal samples obtained from clinical monitoring systems were analysed as part of a crowdsourced artificial intelligence (AI) challenge. Participants were tasked to develop an ictal/interictal classifier with high sensitivity and low false alarm rates. We built a challenge platform that prevented participants from downloading or directly accessing the data while allowing crowdsourced model development. Findings: The automatic detection system achieved tunable sensitivities between 75.00% and 91.60% allowing a reduction in the amount of raw EEG data to be reviewed by a human annotator by factors between 142x, and 22x respectively. The algorithm enables instantaneous reviewer-managed optimization of the balance between sensitivity and the amount of raw EEG data to be reviewed. Interpretation: This study demonstrates the utility of deep learning for patient-specific seizure detection in EEG data. Furthermore, deep learning in combination with a human reviewer can provide the basis for an assistive data labelling system lowering the time of manual review while maintaining human expert annotation performance. Funding: IBM employed all IBM Research authors. Temple University employed all Temple University authors. The Icahn School of Medicine at Mount Sinai employed Eren Ahsen. The corresponding authors Stefan Harrer and Gustavo Stolovitzky declare that they had full access to all the data in the study and that they had final responsibility for the decision to submit for publication.

This paper presents a scratch detection system based on a cubic Bezier curve fitting using a linear scan camera. The objective was to detect the scratch defects of an aluminum flat tube stably in real-time under complex uncertain background noise. To that end, according to the features of the input image of the linear scan camera and the scratch defects, the proposed method first segmented the input image to ten equal sections in a longitudinal direction, and for every section, OTSU thresholding and morphological filtering were used to reduce the background noise. After the image preprocessing, every section image was projected along a vertical direction to form a vertical histogram. After that, for each point of every vertical histogram, a novel curve fitting method based on the Monte Carlo method was employed to calculate the best fitted Bezier curve. All the curvatures of the middle point of the best fitted Bezier curves then formed a curvature curve, and the scratches were located by finding the peaks of the curvature curve. Next, the result of the ten sections were fused to find the final positions of the scratches. The experimental results based on the linear scan camera that captured the image of flat tubes on a moving speed of 2m/s showed that the proposed method can detect the scratch defects under complex background noise with a high success rate in real-time.

In this paper, an optimized three-dimensional (3D) pairwise point cloud registration algorithm is proposed, which is used for flatness measurement based on a laser profilometer. The objective is to achieve a fast and accurate six-degrees-of-freedom (6-DoF) pose estimation of a large-scale planar point cloud to ensure that the flatness measurement is precise. To that end, the proposed algorithm extracts the boundary of the point cloud to obtain more effective feature descriptors of the keypoints. Then, it eliminates the invalid keypoints by neighborhood evaluation to obtain the initial matching point pairs. Thereafter, clustering combined with the geometric consistency constraints of correspondences is conducted to realize coarse registration. Finally, the iterative closest point (ICP) algorithm is used to complete fine registration based on the boundary point cloud. The experimental results demonstrate that the proposed algorithm is superior to the current algorithms in terms of boundary extraction and registration performance.