Life's essential 8 and cardiovascular disease among patients with hyperuricemia: The Kailuan Cohort Study.

OBJECTIVES: Studies have found that a high Life's Essential 8 (LE8) score is associated with a reduced risk of cardiovascular disease(CVD) in cancer populations and young adults. However, the association between LE8 and the risk of CVD in hyperuricemia (HUA) is not fully understood. METHODS: The main analysis included 6814 HUA participants. In a secondary analysis, 5,418 participants were selected from the main analysis to model the trajectory of uric acid (UA) levels from 2006 to 2010. Cox regression model was used to investigate the relationship between LE8 total score and cardiovascular disease risk in different populations. RESULTS: Follow-up of 15.79 years in the main analysis, 986 CVD events occurred. With tertile 1 as the control group, the HR and 95 % CI of CVD in tertile 2 and tertile 3 were 0.75(0.65,0.87) and 0.56(0.47,0.66). In the secondary analysis, the HR and 95 %CI of individuals with low and medium levels of UA reduced CVD were 0.49(0.26,0.89) and 0.56(0.41,0.76), respectively, but this association was not found in individuals with sustained high UA levels. The risk of CVD was different between the sexes. There are differences in cardiovascular disease risk among different age groups. CONCLUSIONS: The risk of CVD in HUA population decreased with the increase of LE8 score, especially in young and middle-aged people and women. However, it is important to note that LE8 may not reduce the risk of CVD in individuals with sustained high UA levels.

Nanoporous Aramid Nanofiber Separators with High Modulus and Thermal Stability for Safe Lithium-Ion Batteries.

Developing high-safety separators is a promising strategy to prevent thermal runaway in lithium-ion batteries (LIBs), which stems from the low melting temperatures and inadequate modulus of commercial polyolefin separators. However, achieving high modulus and thermal stability, along with uniform nanopores in these separators, poses significant challenges. Herein, the study presents ultrathin nanoporous aramid nanofiber (ANF) separators with high modulus and excellent thermal stability, enhancing the safety of LIBs. These separators are produced using a microfluidic-based continuous printing strategy, where the flow thickness can be meticulously controlled at the micrometer scale. This method allows for the continuous fabrication of nanoporous ANF separators with thicknesses ranging from 1.6 ± 0.1 µm to 2.7 ± 0.1 µm. Thanks to the double-side solvent diffusion, the separators exhibit controllably uniform pore sizes with a narrow distribution, spanning from 40 ± 5 nm to 105 ± 9 nm, and a high modulus of 3.3 ± 0.5 GPa. These nanoporous ANF separators effectively inhibit lithium dendrite formation, resulting in a high-capacity retention rate for the LIBs (80% after 240 cycles). Most notably, their robust structural and mechanical stability at elevated temperatures significantly enhances LIB safety under transient thermal abuse conditions, thus addressing critical safety concerns associated with LIBs.

A triple-mode strategy combining low-temperature photothermal, photodynamic, and chemodynamic therapies for treating infectious skin wounds.

The skin is the first natural barrier of the human body. Bacterial infections severely hinder the healing process of skin wounds and pose a great threat to human health. Therefore, it is particularly urgent to develop new antimicrobial strategies for bacterial pathogen clearance and wound healing. In this study, a metal-organic framework (MOF), Fe-MIL88B-NH2, was incorporated with the photosensitizer indocyanine green (ICG) to construct composite nanoparticles (MOF@ICG NPs) with multiple antibacterial activities. Under mild near-infrared (NIR) irradiation, the photosensitizer ICG in the MOF@ICG NPs undergoes photothermal conversion (∼45 °C) and photodynamic reactions to generate heat and singlet oxygen (1O2). In addition, the Fenton reaction of the NPs with hydrogen peroxide (H2O2) in the bacterial infection microenvironment resulted in the generation of hydroxyl radicals (˙OH), thus achieving the three-mode combination of low-temperature photothermal therapy (PTT)/photodynamic therapy (PDT)/chemodynamic therapy (CDT). The in vitro experimental results showed that MOF@ICG MPs had excellent antibacterial properties and good cytocompatibility, with some ability to promote the migration of L-929 fibroblasts. Furthermore, under NIR irradiation, MOF@ICG NPs could significantly kill bacteria and promote skin wound healing according to the results of animal experiments. The wound healing rate reached 87.1% after 7 days of treatment. The research results break through the limitations of single-mode antibacterial technology and provide certain theoretical guidance and technical support for the research and development of new antibacterial materials.

Single-cell RNA sequencing highlights the immunosuppression of IDO1+ macrophages in the malignant transformation of oral leukoplakia.

Rationale: Immunosuppressive tumor microenvironment (iTME) plays an important role in carcinogenesis, and some macrophage subsets are associated with iTME generation. However, the sub-population characterization of macrophages in oral carcinogenesis remains largely unclear. Here, we investigated the immunosuppressive status with focus on function of a macrophage subset that expressed indoleamine 2,3 dioxygenase 1 (Macro-IDO1) in oral carcinogenesis. Methods: We built a single cell transcriptome atlas from 3 patients simultaneously containing oral squamous cell carcinoma (OSCC), precancerous oral leukoplakia (preca-OLK) and paracancerous tissue (PCA). Through single-cell RNA sequencing and further validation using multicolor immunofluorescence staining and the in vitro/in vivo experiments, the immunosuppressive cell profiles were built and the role of a macrophage subset that expressed indoleamine 2,3 dioxygenase 1 (Macro-IDO1) in the malignant transformation of oral leukoplakia was evaluated. Results: The iTME formed at preca-OLK stage, as evidenced by increased exhausted T cells, Tregs and some special subsets of macrophages and fibroblasts. Macro-IDO1 was predominantly enriched in preca-OLK and OSCC, distributed near exhausted T cells and possessed tumor associated macrophage transformation potentials. Functional analysis revealed the established immunosuppressive role of Macro-IDO1 in preca-OLK and OSCC: enriching the immunosuppression related genes; having an established level of immune checkpoint score; exerting strong immunosuppressive interaction with T cells; positively correlating with the CD8-exhausted. The immunosuppression related gene expression of macrophages also increased in preca-OLK/OSCC compared to PCA. The use of the IDO1 inhibitor reduced 4NQO induced oral carcinogenesis in mice. Mechanistically, IFN-γ-JAK-STAT pathway was associated with IDO1 upregulation in OLK and OSCC. Conclusions: These results highlight that Macro-IDO1-enriched in preca-OLK possesses a strong immunosuppressive role and contributes to oral carcinogenesis, providing a potential target for preventing precancerous legions from transformation into OSCC.

Metagenomic functional profiling: to sketch or not to sketch?

MOTIVATION: Functional profiling of metagenomic samples is essential to decipher the functional capabilities of microbial communities. Traditional and more widely used functional profilers in the context of metagenomics rely on aligning reads against a known reference database. However, aligning sequencing reads against a large and fast-growing database is computationally expensive. In general, k-mer-based sketching techniques have been successfully used in metagenomics to address this bottleneck, notably in taxonomic profiling. In this work, we describe leveraging FracMinHash (implemented in sourmash, a publicly available software), a k-mer-sketching algorithm, to obtain functional profiles of metagenome samples. RESULTS: We show how pieces of the sourmash software (and the resulting FracMinHash sketches) can be put together in a pipeline to functionally profile a metagenomic sample. We named our pipeline fmh-funprofiler. We report that the functional profiles obtained using this pipeline demonstrate comparable completeness and better purity compared to the profiles obtained using other alignment-based methods when applied to simulated metagenomic data. We also report that fmh-funprofiler is 39-99× faster in wall-clock time, and consumes up to 40-55× less memory. Coupled with the KEGG database, this method not only replicates fundamental biological insights but also highlights novel signals from the Human Microbiome Project datasets. AVAILABILITY AND IMPLEMENTATION: This fast and lightweight metagenomic functional profiler is freely available and can be accessed here: https://github.com/KoslickiLab/fmh-funprofiler. All scripts of the analyses we present in this manuscript can be found on GitHub.

Symmetric spatiotemporal learning network with sparse meter graph for short-term energy-consumption prediction in manufacturing systems.

Short-term energy-consumption prediction is the basis of anomaly detection, real-time scheduling, and energy-saving control in manufacturing systems. Most existing methods focus on single-node energy-consumption prediction and suffer from difficult parameter collection and modelling. Although several methods have been presented for multinode energy-consumption prediction, their prediction performance needs to be improved owing to a lack of appropriate knowledge guidance and learning networks for complex spatiotemporal relationships. This study presents a symmetric spatiotemporal learning network (SSTLN) with a sparse meter graph (SMG) (SSTLN-SMG) that aims to predict multiple nodes based on energy-consumption time series and general process knowledge. The SMG expresses process knowledge by abstracting production nodes, material flows, and energy usage, and provides initial guidance for the SSTLN to extract spatial features. SSTLN, a symmetrical stack of graph convolutional networks (GCN) and gated linear units (GLU), is devised to achieve a trade-off not only between spatial and temporal feature extraction but also between detail capture and noise suppression. Extensive experiments were performed using datasets from an aluminium profile plant. The experimental results demonstrate that the proposed method allows multinode energy-consumption prediction with less prediction error than state-of-the-art methods, methods with deformed meter graphs, and methods with deformed learning networks.

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).

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.

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.

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.

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.

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.

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.