Digital ray: enhancing cataractous fundus images using style transfer generative adversarial networks to improve retinopathy detection.

BACKGROUND/AIMS: The aim of this study was to develop and evaluate digital ray, based on preoperative and postoperative image pairs using style transfer generative adversarial networks (GANs), to enhance cataractous fundus images for improved retinopathy detection. METHODS: For eligible cataract patients, preoperative and postoperative colour fundus photographs (CFP) and ultra-wide field (UWF) images were captured. Then, both the original CycleGAN and a modified CycleGAN (C2ycleGAN) framework were adopted for image generation and quantitatively compared using Frechet Inception Distance (FID) and Kernel Inception Distance (KID). Additionally, CFP and UWF images from another cataract cohort were used to test model performances. Different panels of ophthalmologists evaluated the quality, authenticity and diagnostic efficacy of the generated images. RESULTS: A total of 959 CFP and 1009 UWF image pairs were included in model development. FID and KID indicated that images generated by C2ycleGAN presented significantly improved quality. Based on ophthalmologists' average ratings, the percentages of inadequate-quality images decreased from 32% to 18.8% for CFP, and from 18.7% to 14.7% for UWF. Only 24.8% and 13.8% of generated CFP and UWF images could be recognised as synthetic. The accuracy of retinopathy detection significantly increased from 78% to 91% for CFP and from 91% to 93% for UWF. For retinopathy subtype diagnosis, the accuracies also increased from 87%-94% to 91%-100% for CFP and from 87%-95% to 93%-97% for UWF. CONCLUSION: Digital ray could generate realistic postoperative CFP and UWF images with enhanced quality and accuracy for overall detection and subtype diagnosis of retinopathies, especially for CFP.\ TRIAL REGISTRATION NUMBER: This study was registered with ClinicalTrials.gov (NCT05491798).

Molecular and immune characterization of Chinese early-stage non-squamous non-small cell lung cancer: a multi-omics cohort study.

Background: Albeit considered with superior survival, around 30% of the early-stage non-squamous non-small cell lung cancer (Ns-NSCLC) patients relapse within 5 years, suggesting unique biology. However, the biological characteristics of early-stage Ns-NSCLC, especially in the Chinese population, are still unclear. Methods: Multi-omics interrogation of early-stage Ns-NSCLC (stage I-III), paired blood samples and normal lung tissues (n=76) by whole-exome sequencing (WES), RNA sequencing, and T-cell receptor (TCR) sequencing were conducted. Results: An average of 128 exonic mutations were identified, and the most frequently mutant gene was EGFR (55%), followed by TP53 (37%) and TTN (26%). Mutations in MUC17, ABCA2, PDE4DIP, and MYO18B predicted significantly unfavorable disease-free survival (DFS). Moreover, cytobands amplifications in 8q24.3, 14q13.1, 14q11.2, and deletion in 3p21.1 were highlighted in recurrent cases. Higher incidence of human leukocyte antigen loss of heterozygosity (HLA-LOH), higher tumor mutational burden (TMB) and tumor neoantigen burden (TNB) were identified in ever-smokers than never-smokers. HLA-LOH also correlated with higher TMB, TNB, intratumoral heterogeneity (ITH), and whole chromosomal instability (wCIN) scores. Interestingly, higher ITH was an independent predictor of better DFS in early-stage Ns-NSCLC. Up-regulation of immune-related genes, including CRABP2, ULBP2, IL31RA, and IL1A, independently portended a dismal prognosis. Enhanced TCR diversity of peripheral blood mononuclear cells (PBMCs) predicted better prognosis, indicative of a noninvasive method for relapse surveillance. Eventually, seven machine-learning (ML) algorithms were employed to evaluate the predictive accuracy of clinical, genomic, transcriptomic, and TCR repertoire data on DFS, showing that clinical and RNA features combination in the random forest (RF) algorithm, with area under the curve (AUC) of 97.5% and 83.3% in the training and testing cohort, respectively, significantly outperformed other methods. Conclusions: This study comprehensively profiled the genomic, transcriptomic, and TCR repertoire spectrums of Chinese early-stage Ns-NSCLC, shedding light on biological underpinnings and candidate biomarkers for prognosis development.

Deep Augmented Metric Learning Network for Prostate Cancer Classification in Ultrasound Images.

Prostate cancer screening often relies on cost-intensive MRIs and invasive needle biopsies. Transrectal ultrasound imaging, as a more affordable and non-invasive alternative, faces the challenge of high inter-class similarity and intra-class variability between benign and malignant prostate cancers. This complexity requires more stringent differentiation of subtle features for accurate auxiliary diagnosis. In response, we introduce the novel Deep Augmented Metric Learning (DAML) network, specifically tailored for ultrasound-based prostate cancer classification. The DAML network represents a significant innovation in the metric learning space, introducing the Semantic Differences Mining Strategy (SDMS) to effectively discern and represent subtle differences in prostate ultrasound images, thereby enhancing tumor classification accuracy. Additionally, the DAML network strategically addresses class variability and limited sample sizes by combining the Linear Interpolation Augmentation Strategy (LIAS) and Permutation-Aided Reconstruction Loss (PARL). This approach enriches feature representation and introduces variability with straightforward structures, mirroring the efficacy of advanced sample generation techniques. We carried out comprehensive empirical assessments of the DAML model by testing its key components against a range of models, ensuring its effectiveness. Our results demonstrate the enhanced performance of the DAML model, achieving classification accuracies of 0.857 and 0.888 for benign and malignant cancers, respectively, underscoring its effectiveness in prostate cancer classification via medical imaging.

MetagenomicKG: a knowledge graph for metagenomic applications.

Motivation: The sheer volume and variety of genomic content within microbial communities makes metagenomics a field rich in biomedical knowledge. To traverse these complex communities and their vast unknowns, metagenomic studies often depend on distinct reference databases, such as the Genome Taxonomy Database (GTDB), the Kyoto Encyclopedia of Genes and Genomes (KEGG), and the Bacterial and Viral Bioinformatics Resource Center (BV-BRC), for various analytical purposes. These databases are crucial for genetic and functional annotation of microbial communities. Nevertheless, the inconsistent nomenclature or identifiers of these databases present challenges for effective integration, representation, and utilization. Knowledge graphs (KGs) offer an appropriate solution by organizing biological entities and their interrelations into a cohesive network. The graph structure not only facilitates the unveiling of hidden patterns but also enriches our biological understanding with deeper insights. Despite KGs having shown potential in various biomedical fields, their application in metagenomics remains underexplored. Results: We present MetagenomicKG, a novel knowledge graph specifically tailored for metagenomic analysis. MetagenomicKG integrates taxonomic, functional, and pathogenesis-related information from widely used databases, and further links these with established biomedical knowledge graphs to expand biological connections. Through several use cases, we demonstrate its utility in enabling hypothesis generation regarding the relationships between microbes and diseases, generating sample-specific graph embeddings, and providing robust pathogen prediction. Availability and Implementation: The source code and technical details for constructing the MetagenomicKG and reproducing all analyses are available at Github: https://github.com/KoslickiLab/MetagenomicKG. We also host a Neo4j instance: http://mkg.cse.psu.edu:7474 for accessing and querying this graph.

Tunable molecular editing of indoles with fluoroalkyl carbenes.

Building molecular complexity from simple feedstocks through precise peripheral and skeletal modifications is central to modern organic synthesis. Nevertheless, a controllable strategy through which both the core skeleton and the periphery of an aromatic heterocycle can be modified with a common substrate remains elusive, despite its potential to maximize structural diversity and applications. Here we report a carbene-initiated chemodivergent molecular editing of indoles that allows both skeletal and peripheral editing by trapping an electrophilic fluoroalkyl carbene generated in situ from fluoroalkyl N-triftosylhydrazones. A variety of fluorine-containing N-heterocyclic scaffolds have been efficiently achieved through tunable chemoselective editing reactions at the skeleton or periphery of indoles, including one-carbon insertion, C3 gem-difluoroolefination, tandem cyclopropanation and N1 gem-difluoroolefination, and cyclopropanation. The power of this chemodivergent molecular editing strategy has been highlighted through the modification of the skeleton or periphery of natural products in a controllable and chemoselective manner. The reaction mechanism and origins of the chemo- and regioselectivity have been probed by both experimental and theoretical methods.

Regulation of rheumatoid arthritis microenvironment via a self-healing injectable hydrogel for improved inflammation elimination and bone repair.

The rheumatoid arthritis (RA) microenvironment is often followed by a vicious circle of high inflammation, endogenous gas levels imbalance, and poor treatment. To break the circle, we develop a dual-gas-mediated injectable hydrogel for modulating the immune microenvironment of RA and simultaneously releasing therapeutic drugs. The hydrogel (DNRS gel) could be broken down on-demand by consuming excessive nitric oxide (NO) and releasing therapeutic hydrogen sulfide (H2S), resulting in endogenous gas restoration, inflammation alleviation, and macrophage polarization to M2 type. Additionally, the hydrogel could suppress osteoclastogenesis and enhance osteogenesis. Furthermore, the intra-articularly injected hydrogel with methotrexate (MTX/DNRS gel) significantly alleviated inflammation and clinical symptoms and promoted the repair of bone erosion in the collagen-induced arthritis rat model. As a result, in vivo results demonstrated that MTX/DNRS gel restored the microenvironment and improved the therapeutic effect of MTX. This study provides a novel understanding of developing versatile smart delivery platforms for RA treatment.

Titanium-based substrate modified with nanoenzyme for accelerating the repair of bone defect.

Titanium (Ti) and titanium alloy are the most common metal materials in clinical orthopedic surgery. However, in the initial stage of surgery and implantation, the production of excessive reactive oxygen species (ROS) can induce oxidative stress (OS) microenvironment. OS will further inhibit the growth of new bone, resulting in surgical failure. In this study, based on the fact that nanoscale manganese dioxide (MnO2) can show H2O2-like enzyme activity, a MnO2 nanocoating was prepared on mciro-nano structured surface of Ti substrate via a two-step method of alkaline thermal and hydrothermal treatment. The results of scanning electron microscopy (SEM), X-ray diffractometer (XRD) and X-ray photoelectron spectroscopy (XPS) showed that the nano-MnO2 coating was successfully fabricated on the surface of Ti substrate. The results of measurement of H2O2, dissolved O2 and intracellular ROS in vitro showed that the treated Ti substrate could efficiently eliminate H2O2 and reduce ROS. Furthermore, the modified Ti substrate could promote the early adhesion, proliferation and osteogenic differentiation of MSCs, which was demonstrated by experimental results of cell morphology, cell viability, alkaline phosphatase, collagen, and mineralization deposition. The results of quantitative real-time polymerase chain reaction (qRT-PCR) of MSCs adhered the modified Ti substrate showed that the expression of genes related to osteogenic differentiation significantly increased. More importantly, the modified Ti implant could eliminate ROS at the injury site, reduce OS and promote the regeneration of bone tissue, which was demonstrated via hematoxylin/eosin, Masson's trichrome and immunohistochemical staining. In conclusion, the modified Ti implant presented here had the effect of reducing OS and promoting osseointegration. Relevant research ideas and results provide new methods for the research and development of functional implants, which have potential application value in the field of orthopedics.

Superspreading-Based Fabrication of Thermostable Nanoporous Polyimide Membranes for High Safety Separators of Lithium-Ion Batteries.

The development of thermally stable separators is a promising approach to address the safety issues of lithium-ion batteries (LIBs) owing to the serious shrinkage of commercial polyolefin separators at elevated temperatures. However, achieving controlled nanopores with a uniform size distribution in thermostable polymeric separators and high electrochemical performance is still a great challenge. In this study, nanoporous polyimide (PI) membranes with excellent thermal stability as high-safety separators is developed for LIBs using a superspreading strategy. The superspreading of polyamic acid solutions enables the generation of thin and uniform liquid layers, facilitating the formation of thin PI membranes with controllable and uniform nanopores with narrow size distribution ranging from 121 ± 5 nm to 86 ± 6 nm. Such nanoporous PI membranes display excellent structural stability at elevated temperatures up to 300 °C for at least 1 h. LIBs assembled with nanoporous PI membranes as separators show high specific capacity and Coulombic efficiency and can work normally after transient treatment at a high temperature (150 °C for 20 min) and high ambient temperature, indicating their promising application as high-safety separators for rechargeable batteries.

Synergistic D-Amino Acids Based Antimicrobial Cocktails Formulated via High-Throughput Screening and Machine Learning.

Antimicrobial resistance (AMR) from pathogenic bacterial biofilms has become a global health issue while developing novel antimicrobials is inefficient and costly. Combining existing multiple drugs with enhanced efficacy and/or reduced toxicity may be a promising approach to treat AMR. D-amino acids mixtures coupled with antibiotics can provide new therapies for drug-resistance infection with reduced toxicity by lower drug dosage requirements. However, iterative trial-and-error experiments are not tenable to prioritize credible drug formulations, owing to the extremely large number of possible combinations. Herein, a new avenue is provide to accelerate the exploration of desirable antimicrobial formulations via high-throughput screening and machine learning optimization. Such an intelligent method can navigate the large search space and rapidly identify the D-amino acid mixtures with the highest anti-biofilm efficiency and also the synergisms between D-amino acid mixtures and antibiotics. The optimized drug cocktails exhibit high antimicrobial efficacy while remaining non-toxic, which is demonstrated not only from in vitro assessments but also the first in vivo study using a lung infection mouse model.

Ternary low-temperature phototherapy nano-systems for the treatment of diabetic wounds.

The physicochemical environment at the sites of chronic diabetic wounds is an ideal habitat for bacteria, which exacerbate the deterioration of the microenvironment at the wound sites and consequently delay wound healing. In recent years, photothermal therapy has been considered an ideal non-antibiotic antimicrobial strategy. However, photothermal therapy alone is prone to cause damage to the body tissues. Herein, a (zeolitic imidazolate framework-8) ZIF-8/(mesoporous polydopamine) MPDA@(deoxyribonuclease I) DNase I ternary nanocomposite system was constructed, which exhibited good antimicrobial and antioxidant properties. Specifically, DNase I was first encapsulated into MPDA nanoparticles (NPs) and then coated with ZIF-8, which rapidly degrades in an acidic bacterial environment, triggering the release of antimicrobial Zn2+ and DNase I, thus enabling low-temperature (∼45 °C) PTT antimicrobial therapy. Meanwhile, the NPs can effectively regulate the oxidative stress environment at the trauma site because of the antioxidant effect of MPDA. Moreover, the experimental results of the diabetic wound infection mouse model showed that the prepared NPs could kill bacteria well and accelerate wound healing. Overall, the phototherapy strategy proposed in this study shows great potential in the treatment of chronically infected wounds.

MOPDDI: Predicting Drug-Drug Interaction Events Based on Multimodal Mutual Orthogonal Projection and Intermodal Consistency Loss.

Predicting accurately the mechanisms of drug-drug interaction (DDI) events is crucial in drug research and development. Existing methods used to predict these events are primarily based on deep learning and have achieved satisfactory results. However, they rarely consider the presence of redundant co-information between the multimodal data of a drug and the need for consistency in the predicted features of each drug modality. Herein, we propose a new method for drug interaction event prediction based on multimodal mutual orthogonal projection and intermodal consistency loss. Our method obtains the features of each modality through a multimodal mutual orthogonal projection module, which eliminates redundant common information with other modalities. In addition, we use the consistency loss between modalities and make the predicted features of each modality more similar. In comparative experiments, our proposed method achieves a prediction accuracy of 0.9500, and an area under the precision-recall (AUPR) curve is 0.9833 for known DDIs. This method outperforms existing methods. The results show that the proposed method is capable of accurately predicting DDIs. The source code is available at https://github.com/xiaqixiaqi/MOPDDI.

Micronano Titanium Accelerates Mesenchymal Stem Cells Aging through the Activation of Senescence-Associated Secretory Phenotype.

Stem cell senescence is one of the most representative events of organism aging and is responsible for many physiological abnormalities and disorders. In the scenario of orthopedic disease treatment, stem cell aging may affect the implantation outcome and even lead to operation failure. To explore whether stem cell aging will affect the osteointegration effect of titanium implant, a widely used micronano titanium (MNT) was fabricated. We first verified the expected osteointegration effect of the MNT, which could be attributed to the improvement of stem cell adhesion and osteogenic differentiation. Then, we obtained aged-derived bone marrow mesenchymal stem cells (BMSCs) and studied their biological behaviors on MNT both in vitro and in vivo. We found that compared with normal rats, MNT did not significantly improve the osteointegration in aged rats. Compared with normal rats, fewer endogenous stem cells were observed at the implant-host interface, and the expression of p21 (senescence marker) was also higher. We further confirmed that MNT promoted the nuclear localization of NF-κB in senescent stem cells through the activation of p38 MAPK, thereby inducing the occurrence of the senescence-associated secretory phenotype (SASP) and ultimately leading to the depletion of the stem-cell pool at the implant-host interface. However, the activation of p38 MAPK can still promote the osteogenic differentiation of nonsenescent BMSCs. These results showed an interesting paradoxical balance between osteogenesis and senescence on MNT surfaces and also provided insights for the design of orthopedic implants for aging patients.

All-in-One Engineering Multifunctional Nanoplatforms for Sensitizing Tumor Low-Temperature Photothermal Therapy In Vivo.

Low-temperature photothermal therapy (PTT) is a noninvasive method that harnesses the photothermal effect at low temperatures to selectively eliminate tumor cells, while safeguarding normal tissues, minimizing thermal damage, and enhancing treatment safety. First we evaluated the transcriptome of tumor cells at the gene level following low-temperature treatment and observed significant enrichment of genes involved in cell cycle and heat response-related signaling pathways. To address this challenge, we have developed an engineering multifunctional nanoplatform that offered an all-in-one strategy for efficient sensitization of low-temperature PTT. Specifically, we utilized MoS2 nanoparticles as the photothermal core to generate low temperature (40-48 °C). The nanoplatform was coated with DPA to load CPT-11 and Fe2+ and was further modified with PEG and iRGD to enhance tumor specificity (MoS2/Fe@CPT-11-PEG-iRGD). Laser- and acid-triggered release of CPT-11 can significantly increase intracellular H2O2 content, cooperate with Fe2+ ions to increase intracellular lipid ROS content, and activate ferroptosis. Furthermore, CPT-11 induced cell cycle arrest in the temperature-sensitive S-phase, and increased lipid ROS levels contributed to the degradation of HSPs protein expression. This synergistic approach could effectively induce tumor cell death by the sensitized low-temperature PTT and the combination of ferroptosis and chemotherapy. Our nanoplatform can also maximize tumor cell eradication and prolong the survival time of tumor-bearing mice in vivo. The multifunctional approach will provide more possibilities for clinical applications of low-temperature PTT and potential avenues for the development of multiple tumor treatments.

Self-supervised dual-head attentional bootstrap learning network for prostate cancer screening in transrectal ultrasound images.

Current convolutional neural network-based ultrasound automatic classification models for prostate cancer often rely on extensive manual labeling. Although Self-supervised Learning (SSL) have shown promise in addressing this problem, those data that from medical scenarios contains intra-class similarity conflicts, so using loss calculations directly that include positive and negative sample pairs can mislead training. SSL method tends to focus on global consistency at the image level and does not consider the internal informative relationships of the feature map. To improve the efficiency of prostate cancer diagnosis, using SSL method to learn key diagnostic information in ultrasound images, we proposed a self-supervised dual-head attentional bootstrap learning network (SDABL), including Online-Net and Target-Net. Self-Position Attention Module (SPAM) and adaptive maximum channel attention module (CAAM) are inserted in both paths simultaneously. They captures position and inter-channel attention and of the original feature map with a small number of parameters, solve the information optimization problem of feature maps in SSL. In loss calculations, we discard the construction of negative sample pairs, and instead guide the network to learn the consistency of the location space and channel space by drawing closer to the embedding representation of positive samples continuously. We conducted numerous experiments on the prostate Transrectal ultrasound (TRUS) dataset, experiments show that our SDABL pre-training method has significant advantages over both mainstream contrast learning methods and other attention-based methods. Specifically, the SDABL pre-trained backbone achieves 80.46% accuracy on our TRUS dataset after fine-tuning.

Deep learning system for distinguishing optic neuritis from non-arteritic anterior ischemic optic neuropathy at acute phase based on fundus photographs.

Purpose: To develop a deep learning system to differentiate demyelinating optic neuritis (ON) and non-arteritic anterior ischemic optic neuropathy (NAION) with overlapping clinical profiles at the acute phase. Methods: We developed a deep learning system (ONION) to distinguish ON from NAION at the acute phase. Color fundus photographs (CFPs) from 871 eyes of 547 patients were included, including 396 ON from 232 patients and 475 NAION from 315 patients. Efficientnet-B0 was used to train the model, and the performance was measured by calculating the sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Also, Cohen's kappa coefficients were obtained to compare the system's performance to that of different ophthalmologists. Results: In the validation data set, the ONION system distinguished between acute ON and NAION achieved the following mean performance: time-consuming (23 s), AUC 0.903 (95% CI 0.827-0.947), sensitivity 0.796 (95% CI 0.704-0.864), and specificity 0.865 (95% CI 0.783-0.920). Testing data set: time-consuming (17 s), AUC 0.902 (95% CI 0.832-0.944), sensitivity 0.814 (95% CI 0.732-0.875), and specificity 0.841 (95% CI 0.762-0.897). The performance (κ = 0.805) was comparable to that of a retinal expert (κ = 0.749) and was better than the other four ophthalmologists (κ = 0.309-0.609). Conclusion: The ONION system performed satisfactorily distinguishing ON from NAION at the acute phase. It might greatly benefit the challenging differentiation between ON and NAION.

Cellular dynamics in tumour microenvironment along with lung cancer progression underscore spatial and evolutionary heterogeneity of neutrophil.

BACKGROUND: The cellular dynamics in the tumour microenvironment (TME) along with non-small cell lung cancer (NSCLC) progression remain unclear. METHODS: Multiplex immunofluorescence test detecting 10 immune-related markers on 553 primary tumour (PT) samples of NSCLC was conducted and spatial information in TME was assessed by the StarDist depth learning model. The single-cell transcriptomic atlas of PT (n = 4) and paired tumour-draining lymph nodes (TDLNs) (n = 5 for tumour-invaded, n = 3 for tumour-free) microenvironment was profiled. Various bioinformatics analyses based on Gene Expression Omnibus, TCGA and Array-Express databases were also used to validate the discoveries. RESULTS: Spatial distances of CD4+ T cells-CD38+ T cells, CD4+ T cells-neutrophils and CD38+ T cells-neutrophils prolonged and they were replaced by CD163+ macrophages in PT along with tumour progression. Neutrophils showed unique stage and location-dependent prognostic effects. A high abundance of stromal neutrophils improved disease-free survival in the early-stage, whereas high intratumoural neutrophil infiltrates predicted poor prognosis in the mid-to-late-stage. Significant molecular and functional reprogramming in PT and TDLN microenvironments was observed. Diverse interaction networks mediated by neutrophils were found between positive and negative TDLNs. Five phenotypically and functionally heterogeneous subtypes of tumour-associated neutrophil (TAN) were further identified by pseudotime analysis, including TAN-0 with antigen-presenting function, TAN-1 with strong expression of interferon (IFN)-stimulated genes, the pro-tumour TAN-2 subcluster, the classical subset (TAN-3) and the pro-inflammatory subtype (TAN-4). Loss of IFN-stimulated signature and growing angiogenesis activity were discovered along the transitional trajectory. Eventually, a robust six neutrophil differentiation relevant genes-based model was established, showing that low-risk patients had longer overall survival time and may respond better to immunotherapy. CONCLUSIONS: The cellular composition, spatial location, molecular and functional changes in PT and TDLN microenvironments along with NSCLC progression were deciphered, highlighting the immunoregulatory roles and evolutionary heterogeneity of TANs.

Stereoselective Synthesis of trans-Stilbenes through Silver-Catalyzed Self-Coupling of N-Triftosylhydrazones: An Experimental and Theoretical Study.

A silver-mediated homocoupling of N-triftosylhydrazones for the construction of trans-stilbenes has been presented. This protocol is characterized by its suitability for both inter- and intramolecular reactions, operational simplicity, high efficiency with excellent stereoselectivity, broad substrate scope, and good functional group tolerance. A plausible mechanism involving nucleophilic attack of in situ generated sulfonium ylides on silver carbene was proposed on the basis of experimental results and DFT calculations, which further indicates that π-π stacking interactions play a dominant role in stereoselectivity control.

Afterglow/Photothermal Bifunctional Polymeric Nanoparticles for Precise Postbreast-Conserving Surgery Adjuvant Therapy and Early Recurrence Theranostic.

Adjuvant whole-breast radiotherapy is essential for breast cancer patients who adopted breast-conserving surgery (BCS) to reduce the risk of local recurrences, which however suffer from large-area and highly destructive ionizing radiation-induced adverse events. To tackle this issue, an afterglow/photothermal bifunctional polymeric nanoparticle (APPN) is developed that utilizes nonionizing light for precise afterglow imaging-guided post-BCS adjuvant second near-infrared (NIR-II) photothermal therapy. APPN consists of a tumor cell targeting afterglow agent, which is doped with a NIR dye as an afterglow initiator and a NIR-II light-absorbing semiconducting polymer as a photothermal transducer. Such a design realizes precise afterglow imaging-guided NIR-II photothermal ablation of minimal residual breast tumor foci after BCS, thus achieving complete inhibition of local recurrences. Moreover, APPN enables early diagnosis and treatment of local recurrence after BCS. This study thus provides a nonionizing modality for precision post-BCS adjuvant therapy and early recurrence theranostic.

A Multifunction Hydrogel-Coating Engineered Implant for Rescuing Biofilm Infection and Boosting Osseointegration by Macrophage-Related Immunomodulation.

Innovative methodologies combined with scavenging reactive oxygen species (ROS), alleviating oxidative stress damage and promoting macrophage polarization to M2 phenotype may be ideal for remodeling implant-infected bone tissue. Herein, a functionalization strategy for doping Tannic acid-d-tyrosine nanoparticles with photothermal profile into the hydrogel coating composed of konjac gum and gelatin on the surface of titanium (Ti) substrate is accurately constructed. The prepared hydrogel coating exhibits excellent properties of eliminating biofilm and killing planktonic bacteria, which is based on increasing susceptibility to bacteria by the photothermal effect, biofilm-dissipation effect of D-tyrosine, as well as the bactericidal effect of tannic acid. In addition, the modified Ti substrate has effectively alleviated proinflammatory responses by scavenging intracellular excessive ROS and guiding macrophages polarization toward M2. More interesting, conditioned medium from macrophage indicates that paracrine is conducive to osteogenic proliferation and differentiation of mesenchymal stem cells. Results from rat model of femur infection in vivo demonstrate that the modified Ti implant significantly eliminates the residual bacteria, relieves inflammation, mediates macrophage polarization, and accelerates osseointegration. Altogether, this study exhibits a new perspective for the development of advanced functional implant with great application potential in bone tissue regeneration and repair.

Functional Titanium Substrates Synergetic Photothermal Therapy for Enhanced Antibacterial and Osteogenic Performance via Immunity Regulation.

Implant-associated infections (IAIs) significantly impair the integration between titanium (Ti) implants and bone tissues. Bacteria colonized on the surface of the implant can induce innate immune suppression of the host to resist clearance. Herein, an interfacial functionalization strategy is employed to introduce FeIII TA nanoparticles (NPs) and acetyl Bletilla striata polysaccharide (acBSP) on the Ti substrate to obtain the Ti-TF-acBSP system. Under near-infrared (NIR) irradiation, the hyperthermal effect induced by FeIII TA NPs directly killed bacteria. Meanwhile, macrophages are induced by acBSP to polarize into pro-inflammatory M1 phenotype, which enhanced the phagocytosis ability of macrophages and activated host innate immunity. Moreover, the asBSP instructed macrophages to secrete pro-osteogenic cytokine, which promoted osteogenic differentiation of MSCs. The results of the animal experiment in vivo confirmed that the Ti-TF-acBSP implant effectively eliminated bacterial infection under NIR irradiation, enhanced the expression of pro-inflammatory cytokine, and induced the production of bone-forming related factors. In a word, the functionalized Ti implant not only have a direct bactericidal effect but also regulate macrophage polarization as well as macrophage-mediated bactericidal and osteogenic effect. The strategy of combining photothermal therapy with immunoregulation will present a potential candidate for the development of novel antibacterial orthopedic devices.