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RATIONALE AND OBJECTIVES: Deep learning can enhance the performance of multimodal image analysis, which is known for its noninvasive attributes and complementary efficacy, in predicting axillary lymph node (ALN) metastasis. Therefore, we established a multimodal deep learning model incorporating ultrasound (US) and magnetic resonance imaging (MRI) images to predict ALN metastasis in patients with breast cancer. MATERIALS AND METHODS: A retrospective cohort of patients with histologically confirmed breast cancer from two hospitals composed of the primary cohort (n = 465) and the external validation cohort (n = 123). All patients had undergone both preoperative US and MRI scans. After data preprocessing, three convolutional neural network models were used to analyze the US and MRI images, respectively. After integrating the US and MRI deep learning prediction results (DLUS and DLMRI, respectively), a multimodal deep learning (DLMRI+US+Clinical parameter) model was constructed. The predictive ability of the proposed model was compared to that of the DLUS, DLMRI, combined bimodal (DLMRI+US), and clinical parameter models. Evaluation was performed using the area under the receiver operating characteristic curves (AUCs) and decision curves. RESULTS: A total of 588 patients with breast cancer participated in this study. The DLMRI+US+Clinical parameter model outperformed the alternative models, achieving the highest AUCs of 0.819 (95% confidence interval [CI] 0.734-0.903) and 0.809 (95% CI 0.723-0.895) on the internal and external validation sets, respectively. The decision curve analysis confirmed its clinical usefulness. CONCLUSION: The DLMRI+USï¼Clinical parameter model demonstrates the feasibility and reliability of its performance for ALN metastasis prediction in patients with breast cancer.
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Immune checkpoint inhibitors (ICIs) offer promise in breaking through the treatment and survival dilemma of triple-negative breast cancer (TNBC), yet only immunomodulatory subtype and ≈5% TNBC patients respond as monotherapy due to lack of effector immune cells (internal problem) and physical barrier (external limitation) formed by cancer-associated fibroblasts (CAFs). A hydrogel drug-delivery platform, ALG@TBP-2/Pt(0)/nintedanib (ALG@TPN), is designed to induce strong immune functions and the dual elimination of the internal and external tumor microenvironment (TME). Activated by white light, through type I and II photodynamic therapy (PDT), TBP-2 generates large amounts of reactive oxygen species (ROS) intracellularly, oxidizing mitochondrial DNA (mtDNA). The unique catalase activity of Pt(0) converts endogenous H2O2 to O2, reducing the anoxia-limiting PDT and enhancing ROS generation efficacy. Abundant ROS can oxidize Pt(0) to cytotoxic Pt(II), damaging the nuclear DNA (nDNA). Dual damage to mtDNA and nDNA might bi-directionally activate the cGAS/STING pathway and enhance the immune cell response. Besides, nintedanib demonstrates a significant inhibitory effect on CAFs, weakening the immune barrier and deepening immune cell infiltration. Overall, the study provides a self-oxygenating hydrogel with the "PDT/chemotherapy/anti-CAFs" effect, triggering the cGAS/STING pathway to reshape the TME. Both internal and external interventions increase anti-TNBC immune responses.
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Although the discovery of insulin 100 years ago revolutionized the treatment of diabetes, its therapeutic potential is compromised by its short half-life and narrow therapeutic index. Current long-acting insulin analogs, such as insulin-polymer conjugates, are mainly used to improve pharmacokinetics by reducing renal clearance. However, these conjugates are synthesized without sacrificing the bioactivity of insulin, thus retaining the narrow therapeutic index of native insulin, and exceeding the efficacious dose still leads to hypoglycemia. Here, we report a kind of di-PEGylated insulin that can simultaneously reduce renal clearance and receptor-mediated clearance. By impairing the binding affinity to the receptor and the activation of the receptor, di-PEGylated insulin not only further prolongs the half-life of insulin compared to classical mono-PEGylated insulin but most importantly, increases its maximum tolerated dose 10-fold. The target of long-term glycemic management in vivo has been achieved through improved pharmacokinetics and a high dose. This work represents an essential step towards long-acting insulin medication with superior safety in reducing hypoglycemic events.
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PURPOSE: To investigate the feasibility of using radiomics analysis of quantitative maps from synthetic MRI to preoperatively predict diffuse glioma grades, isocitrate dehydrogenase (IDH) subtypes, and 1p/19q codeletion status. METHODS: Data from 124 patients with diffuse glioma were used for analysis (nâ¯= 87 for training, nâ¯= 37 for testing). Quantitative T1, T2, and proton density (PD) maps were obtained using synthetic MRI. Enhancing tumour (ET), non-enhancing tumour and necrosis (NET), and peritumoral edema (PE) regions were segmented followed by manual fine-tuning. Features were extracted using PyRadiomics and then selected using Levene/T, BorutaShap and maximum relevance minimum redundancy algorithms. A support vector machine was adopted for classification. Receiver operating characteristic curve analysis and integrated discrimination improvement analysis were implemented to compare the performance of different radiomics models. RESULTS: Radiomics models constructed using features from multiple tumour subregions (ETâ¯+ NETâ¯+ PE) in the combined maps (T1â¯+ T2â¯+ PD) achieved the highest AUC in all three prediction tasks, among which the AUC for differentiating lower-grade and high-grade diffuse gliomas, predicting IDH mutation status and predicting 1p/19q codeletion status were 0.92, 0.95 and 0.86 respectively. Compared with those constructed on individual T1, T2, and PD maps, the discriminant ability of radiomics models constructed on the combined maps separately increased by 11, 17 and 10% in predicting glioma grades, 35, 52 and 19% in predicting IDH mutation status, and 16, 15 and 14% in predicting 1p/19q codeletion status (pâ¯< 0.05). CONCLUSION: Radiomics analysis of quantitative maps from synthetic MRI provides a new quantitative imaging tool for the preoperative prediction of grades and molecular subtypes in diffuse gliomas.
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The advantages of small extracellular vesicles (sEV) in disease management have become increasingly prominent, with the main challenge lying in meeting the demands of large-scale extraction and high-throughput analysis, a crucial aspect in the realm of precision medicine. To overcome this challenge, an engineered on-plate aptamer array (16×24 spots) is developed for continuous scale-up microextraction of plasma sEV and their in situ metabolic analysis using mass spectrometry. With this integrated array strategy, metabolic profiles of sEV are acquired from the plasma of 274 antenatal or postpartum women, reducing analysis time by half (7.5 h) and sample volume by 95% (only 0.125 µL usage) compared to the traditional suspension method. Moreover, using machine learning algorithms on sEV metabolic profiles, a risk score system is constructed that accurately assesses the need for epidural analgesia during childbirth and the likelihood of post-administration fever. The system, based on admission samples, achieves an impressive 94% accuracy. Furthermore, post-administration fever can be identified from delivery samples, reaching an overall accuracy rate of 88%. This work offers real-time monitoring of the childbirth process that can provide timely guidance for maternal delivery, underscoring the significance of sEV detection in large-scale clinical samples for medicine innovation and advancement.
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Platelet-derived growth factor B (PDGFB), as an important cellular growth factor, is widely involved in the regulation of cellular events such as cell growth, proliferation, and differentiation. Although important, the expression characteristics and biological functions in the mammalian reproductive system remain poorly understood. In this study, the PDGFB gene of Tibetan sheep was cloned by RT-PCR, and its molecular characteristics were analyzed. Subsequently, the expression of the PDGFB gene in the testes and epididymides (caput, corpus, and cauda) of Tibetan sheep at different developmental stages (3 months, 1 year, and 3 years) was examined by qRT-PCR and immunofluorescence staining. A bioinformatic analysis of the cloned sequences revealed that the CDS region of the Tibetan sheep PDGFB gene is 726 bp in length and encodes 241 amino acids with high homology to other mammals, particularly goats and antelopes. With the increase in age, PDGFB expression showed an overall trend of first decreasing and then increasing in the testis and epididymis tissues of Tibetan sheep, and the PDGFB mRNA expression at 3 months of age was extremely significantly higher than that at 1 and 3 years of age (p < 0.05). The PDGFB protein is mainly distributed in testicular red blood cells and Leydig cells in Tibetan sheep at all stages of development, as well as red blood cells in the blood vessel, principal cells, and the pseudostratified columnar ciliated epithelial cells of each epididymal duct epithelium. In addition, PDGFB protein expression was also detected in the spermatocytes of the 3-month-old group, spermatids of the 1-year-old group, spermatozoa and interstitial cells of the 3-year-old group, and loose connective tissue in the epididymal duct space in each developmental period. The above results suggest that the PDGFB gene, as an evolutionarily conserved gene, may play multiple roles in the development and functional maintenance of testicular cells (such as red blood cells, Leydig cells, and germ cells) and epididymal cells (such as red blood cells, principal cells, and ciliated epithelial cells) during testicular and epididymal development, which lays a foundation for the further exploration of the mechanisms by which the PDGFB gene influences spermatogenesis in Tibetan sheep.
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Epstein-Barr virus (EBV) infects more than 95% of adults worldwide and is closely associated with various malignancies. Considering the complex life cycle of EBV, developing vaccines targeting key entry glycoproteins to elicit robust and durable adaptive immune responses may provide better protection. EBV gHgL-, gB- and gp42-specific antibodies in healthy EBV carriers contributed to sera neutralizing abilities in vitro, indicating that they are potential antigen candidates. To enhance the immunogenicity of these antigens, we formulate three nanovaccines by co-delivering molecular adjuvants (CpG and MPLA) and antigens (gHgL, gB or gp42). These nanovaccines induce robust humoral and cellular responses through efficient activation of dendritic cells and germinal center response. Importantly, these nanovaccines generate high levels of neutralizing antibodies recognizing vulnerable sites of all three antigens. IgGs induced by a cocktail vaccine containing three nanovaccines confer superior protection from lethal EBV challenge in female humanized mice compared to IgG elicited by individual NP-gHgL, NP-gB and NP-gp42. Importantly, serum antibodies elicited by cocktail nanovaccine immunization confer durable protection against EBV-associated lymphoma. Overall, the cocktail nanovaccine shows robust immunogenicity and is a promising candidate for further clinical trials.
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Anticuerpos Neutralizantes , Anticuerpos Antivirales , Infecciones por Virus de Epstein-Barr , Glicoproteínas , Nanovacunas , Animales , Femenino , Humanos , Ratones , Adyuvantes Inmunológicos/administración & dosificación , Anticuerpos Neutralizantes/inmunología , Anticuerpos Antivirales/inmunología , Anticuerpos Antivirales/sangre , Infecciones por Virus de Epstein-Barr/inmunología , Infecciones por Virus de Epstein-Barr/prevención & control , Infecciones por Virus de Epstein-Barr/virología , Glicoproteínas/inmunología , Glicoproteínas/administración & dosificación , Herpesvirus Humano 4/inmunología , Linfoma/inmunología , Linfoma/virología , Nanovacunas/inmunologíaRESUMEN
The driver's takeover time is crucial to ensure a safe takeover transition in conditional automated driving. The study aimed to construct a prediction model of driver's takeover time based on individual characteristics, external environment, and situation awareness variables. A total of 18 takeover events were designed with scenarios, non-driving-related tasks, takeover request time, and traffic flow as variables. High-fidelity driving simulation experiments were carried out, through which the driver's takeover data was obtained. Fifteen basic factors and three dynamic factors were extracted from individual characteristics, external environment, and situation awareness. In this experiment, these 18 factors were selected as input variables, and XGBoost and Shapely were used as prediction methods. A takeover time prediction model (BMâ¯+â¯SA model) was then constructed. Moreover, we analyzed the main effect of input variables on takeover time, and the interactive contribution made by the variables. And in this experiment, the 15 basic factors were selected as input variables, and the basic takeover time prediction model (BM model) was constructed. In addition, this study compared the performance of the two models and analyzed the contribution of input variables to takeover time. The results showed that the goodness of fit of the BMâ¯+â¯SA model (Adjusted_R2) was 0.7746. The XGBoost model performs better than other models (support vector machine, random forest, CatBoost, and LightBoost models). The relative importance degree of situation awareness variables, individual characteristic variables, and external environment variables to takeover time gradually reduced. Takeover time increased with the scan and gaze durations and decreased with pupil area and self-reported situation awareness scores. There was also an interaction effect between the variables to affect takeover time. Overall, the performance of the BMâ¯+â¯SA model was better than that of the BM model. This study can provide support for predicting driver's takeover time and analyzing the mechanism of influence on takeover time. This study can provide support for the development of real-time driver's takeover ability prediction systems and optimization of human-machine interaction design in automated vehicles, as well as for the management department to evaluate and improve the driver's takeover performance in a targeted manner.
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Conducción de Automóvil , Concienciación , Humanos , Conducción de Automóvil/psicología , Masculino , Adulto , Femenino , Factores de Tiempo , Simulación por Computador , Adulto Joven , Ambiente , Modelos Teóricos , AutomatizaciónRESUMEN
In brief: Progenitor cells with ovulation-related tissue repair activity were identified with defined markers (LGR5, EPCR, LY6A, and PDGFRA), but their potentials to form steroidogenic cells were not known. This study shows that the cells can generate progenies with different steroidogenic activities. Abstract: Adult mammalian ovaries contain stem/progenitor cells necessary for folliculogenesis and ovulation-related tissue rupture repair. Theca cells are recruited and developed from progenitors during the folliculogenesis. Theca cell progenitors were not well defined. The aim of current study is to compare the potentials of four ovarian progenitors with defined markers (LY6A, EPCR, LGR5, and PDGFRA) to form steroidogenic theca cells in vitro. The location of the progenitors with defined makers was determined by immunohistochemistry and immunofluorescence staining of ovarian sections of adult mice. Different progenitor populations were purified by magnetic-activated cell sorting (MACS) and/or fluorescence-activated cell sorting (FACS) techniques from ovarian cell preparation and were tested for their abilities to generate steroidogenic theca cells in vitro. The cells were differentiated with a medium containing LH, ITS, and DHH agonist for 12 days. The results showed that EPCR+ and LGR5+ cells primarily distributed along the ovarian surface epithelium (OSE), while LY6A+ cells distributed in both the OSE and parenchyma. However, PDGFRA+ cells were exclusively located in interstitial compartment. When the progenitors were purified by these markers and differentiated in vitro, LY6A+ and PDGFRA+ cells formed steroidogenic cells expressing both CYP11A1 and CYP17A1 and primarily producing androgens, showing characteristics of theca-like cells, while LGR5+ cells generated steroidogenic cells devoid of CYP17A1 expression and androgen production, showing a characteristic of progesterone-producing cells (granulosa- or lutea-like cells). In conclusion, progenitors from both OSE and parenchyma of adult mice are capable of generating steroidogenic cells with different steroidogenic capacities, showing a possible lineage preference.
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Diferenciación Celular , Receptores Acoplados a Proteínas G , Células Madre , Células Tecales , Animales , Femenino , Células Tecales/metabolismo , Células Tecales/citología , Ratones , Células Madre/metabolismo , Células Madre/citología , Receptores Acoplados a Proteínas G/metabolismo , Receptores Acoplados a Proteínas G/genética , Antígenos Ly/metabolismo , Células Cultivadas , Receptor alfa de Factor de Crecimiento Derivado de Plaquetas/metabolismo , Ovario/citología , Ovario/metabolismo , Ratones Endogámicos C57BL , Biomarcadores/metabolismoRESUMEN
This study seeks to assess both environmental and economic effects associated with installing photovoltaic systems within construction waste landfills in Macau by employing an effective carbon emissions calculation methodology and benefit analysis method. Beginning by outlining characteristics and challenges associated with construction waste landfills, as well as photovoltaic systems used for this application in this paper. Here, we present a detailed outline of our methodology design, outlining its principles of life cycle analysis, data collection processes and the creation of carbon emissions calculation models. Subsequently, we examine photovoltaic systems within Macau's construction waste landfills by studying system design, component selection and operational strategies as well as carbon emission data collection during their operational time period. Under life cycle carbon emissions calculations, we assess the carbon emissions generated from photovoltaic systems as well as conduct an environmental and economic benefit analysis for carbon reduction benefit analysis purposes. This research incorporates sensitivity analysis and uncertainty consideration in order to conduct an extensive benefit analysis. The research results offer strong support for sustainable photovoltaic systems within Macau waste landfills as well as insights to inform planning and policy formation for similar future projects.
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PURPOSE: To investigate the relationship between blood lead levels (BLLs) and IVF clinical outcomes in infertile females and to further explore the possible involvement of granulosa cell (GC) endoplasmic reticulum (ER) stress in the process. METHODS: One hundred twenty-three infertile women undergoing IVF cycles were included in the current study. All participants were divided into three (low, medium, and high) groups determined by BLL tertiles. Gonadotropin releasing hormone (GnRH) agonist regimen for ovarian stimulation was used for all patients, with follicular fluids being collected on the day of oocyte retrieval. Lactate dehydrogenase (LDH) levels in follicular fluid and the endoplasmic reticulum stress-signaling pathway of granulosa cells (GCs) were examined. RESULTS: The oocyte maturation rate and high-quality embryo rate on cleaved stage decreased significantly as BLL increased. For lead levels from low to high, live birth rate (68.29%, 56.10%, 39.02%; P=0.028) showed negative correlations with BLLs. Also, follicular fluid Pb level and LDH level was significantly higher in the high lead group versus the low group. Binomial regression analysis revealed significant negative correlation between BLLs and live birth rate (adjusted OR, 0.38; 95% CI, 0.15-0.95, P=0.038). Further analysis of the endoplasmic reticulum stress (ER stress) signaling pathway of GCs found that expressions of GRP78, total JNK, phosphorylated JNK, and CHOP increased and BCL-2 decreased with increasing BLLs. CONCLUSIONS: BLLs are negatively associated with final clinical outcomes in IVF patients that may be related to increased ER stress response and GC apoptosis. Thus, reducing Pb exposure before IVF procedures may improve final success rates.
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Chaperón BiP del Retículo Endoplásmico , Estrés del Retículo Endoplásmico , Fertilización In Vitro , Líquido Folicular , Células de la Granulosa , Infertilidad Femenina , Plomo , Inducción de la Ovulación , Humanos , Femenino , Células de la Granulosa/metabolismo , Adulto , Infertilidad Femenina/terapia , Infertilidad Femenina/sangre , Infertilidad Femenina/patología , Plomo/sangre , Plomo/toxicidad , Embarazo , Líquido Folicular/metabolismo , Inducción de la Ovulación/métodos , Índice de Embarazo , Recuperación del Oocito , Nacimiento Vivo/genética , Oocitos/crecimiento & desarrollo , Tasa de NatalidadRESUMEN
The rapid advancement in the fabrication and culture of in vitro organs has marked a new era in biomedical research. While strides have been made in creating structurally diverse bioartificial organs, such as the liver, which serves as the focal organ in our study, the field lacks a uniform approach for the predictive assessment of liver function. Our research bridges this gap with the introduction of a novel, machine-learning-based "3P model" framework. This model draws on a decade of experimental data across diverse culture platform studies, aiming to identify critical fabrication parameters affecting liver function, particularly in terms of albumin and urea secretion. Through meticulous statistical analysis, we evaluated the functional sustainability of the in vitro liver models. Despite the diversity of research methodologies and the consequent scarcity of standardized data, our regression model effectively captures the patterns observed in experimental findings. The insights gleaned from our study shed light on optimizing culture conditions and advance the evaluation of the functional maintenance capacity of bioartificial livers. This sets a precedent for future functional evaluations of bioartificial organs using machine learning.
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Órganos Bioartificiales , Hígado Artificial , Hígado , AlbúminasRESUMEN
Exosome metabolite-based liquid biopsy is a promising strategy for large-scale application in practical clinics toward precise medicine. Given the current challenges in successive isolation and analysis of exosomes and their metabolites in this field, we established a low-cost, high-throughput, and rapid platform for serological exosome metabolic biopsy of hepatocellular carcinoma (HCC) via designed core-shell nanoparticles. It starts with the efficient extraction of high-quality serum exosomes and exosome metabolic features, based on which significantly obvious sample clusters are observed by unsupervised cluster analysis. The following integration of feature selection and supervised machine learning enables the identification of six key metabolites and achieves high-performance prediction between HCC, liver cirrhosis, and healthy controls. Specifically, both sensitivity and accuracy achieve 100% among any pairwise intergroup discrimination in a blind test. The quality and reliability of six key metabolites are further evaluated and validated by using different machine learning algorithms and pathway exploration. Our platform contributes to the future growth of new liquid biopsy technologies for precision diagnosis and real-time monitoring of HCC, among other conditions.
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High-throughput detection of large-scale samples is the foundation for rapidly accessing massive metabolic data in precision medicine. Machine learning is a powerful tool for uncovering valuable information hidden within massive data. In this work, we achieved the extraction of a single fingerprinting of 1 µL serum within 5 s through a high-throughput detection platform based on functionalized nanoparticles. We quickly obtained over a thousand serum metabolic fingerprintings (SMFs) including those of individuals with Helicobacter pylori (HP) infection. Combining four classical machine learning models and enrichment analysis, we attempted to extract and confirm useful information behind these SMFs. Based on all fingerprint signals, all four models achieved area under the curve (AUC) values of 0.983-1. In particular, orthogonal partial least squares discriminant analysis (OPLS-DA) model obtained value of 1 in both the discovery and validation sets. Fortunately, we identified six significant metabolic features, all of which can greatly contribute to the monitoring of HP infection, with AUC values ranging from 0.906 to 0.985. The combination of these six significant metabolic features can enable the precise monitoring of HP infection in serum, with over 95 % of accuracy, specificity and sensitivity. The OPLS-DA model displayed optimal performance and the corresponding scatter plot visualized the clear distinction between HP and HC. Interestingly, they exhibit a consistent reduction trend compared to healthy controls, prompting us to explore the possible metabolic pathways and potential mechanism. This work demonstrates the potential alliance between high-throughput detection and machine learning, advancing their application in precision medicine.
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Infecciones por Helicobacter , Helicobacter pylori , Humanos , Infecciones por Helicobacter/diagnóstico , Infecciones por Helicobacter/metabolismo , Análisis de los Mínimos CuadradosRESUMEN
Exosome metabolite-based noninvasive liquid biopsy is an emerging research hotspot that tends to substitute current means in clinics. Nanostructure-based mass spectrometry enables continuous exosome isolation and metabolic profiling with superior analysis speed and high efficiency. Herein, we construct a heterogeneous MXene hybrid that possesses ternary binding sites for exosome capture and outstanding matrix performance for metabolite analysis. Upon optimizing experimental conditions, the average extraction of exosomes and their metabolic patterns from a 60 mL urine sample is completed within 45 s (40 samples per batch for 30 min). According to the exosomal metabolic patterns and the subsequently established biomarker panel, we distinguish early bladder cancer (BCa) from healthy controls with an area under the curve (AUC) value greater than 0.995 in model training and validation sets. As well, we realize subtype classification of BCa in the blind test on metabolic patterns, with an AUC value of 0.867. We also explore the significant biomarkers that are sensitive to follow-up patients, which indeed present reverse change levels compared with pathological progression. This study has the potential to guide the development of the liquid biopsy approach.
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Exosomas , Neoplasias de la Vejiga Urinaria , Humanos , Exosomas/metabolismo , Estudios de Seguimiento , Detección Precoz del Cáncer , Neoplasias de la Vejiga Urinaria/patología , Biomarcadores/análisis , Biomarcadores de Tumor/análisisRESUMEN
The effect of heavy metal cadmium (Cd) on testicular function is recognized. However, the mechanism involved is not well-established. In the present study, we analyzed the testicular transcriptomic changes induced by acute Cd exposure of adult rats with and without supplementation of antioxidants selenium (Se) and/or coenzyme Q10 (CoQ). Cd significantly decreased serum testosterone and two steroidogenic proteins SCARB1 and STAR. RNA-Seq analyses of testicular RNAs revealed specific activation of oxidative stress-, inflammation-, MAPK- and NF-κB-related signaling molecules. In addition, Cd treatment down-regulated gene for I, III and IV complexes of mitochondrial electron transport chain and up-regulated genes for NADPH-oxidase, major cascade in ROS production. The decrease in steroidogenesis and increase in inflammation may result from oxidative stress since supplementation of Se and CoQ, but not with either alone, almost completely prevented these changes, including overall alterations in transcriptome. Cd exposure induced total of 1192 differentially expressed genes (DEGs), which was reduced to 29 without considering confounding factors associated with Se/CoQ, a 97.6% protection rate. In conclusion, Cd exposure inhibited Leydig cell steroidogenesis by down-regulating SCARB1 and STAR through increasing oxidative stress and inflammation, but Se plus CoQ synergistically prevented all the changes induced by the Cd exposure.
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Cadmio , Selenio , Masculino , Ratas , Animales , Cadmio/toxicidad , Selenito de Sodio/farmacología , Transcriptoma , Antioxidantes/farmacología , Selenio/farmacología , Estrés Oxidativo , Inflamación , Perfilación de la Expresión GénicaRESUMEN
Teeth staining is a common dental health challenge in many parts of the world. Traditional teeth whitening techniques often lead to enamel damage and soft tissue toxicity due to the use of bioincompatible whitening reagents and continuous strong light irradiation. Herein, an "afterglow" photodynamic therapy (aPDT) for teeth whitening is proposed, which is realized by energy transition pathways of intersystem crossing. The covalent and hydrogen bonds formed by carbon dots embedded in silica nanoparticles (CDs@SiO2) facilitate the passage of energy through intersystem crossing (ISC), thereby extending the half-life of reactive oxygen species (ROS). The degradation efficiency of aPDT on dyes was higher than 95% in all cases. It can thoroughly whiten teeth by eliminating stains deep in the enamel without damaging the enamel structure and causing any tissue toxicity. This study illustrates the superiority of aPDT for dental whitening and the approach to exploring carbon-dots-based nanostructures in the treatment of oral diseases.
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Nanopartículas , Fotoquimioterapia , Blanqueamiento de Dientes , Blanqueamiento de Dientes/métodos , Dióxido de Silicio , Carbono , Fotoquimioterapia/métodosRESUMEN
Metal-organic framework-based metal ion therapy has attracted increasing attention to promote the cascade wound-healing process. However, multimetal ion synergistic administration and accurately controlled ion release are still the challenges. Herein, an aptamer-functionalized silver@cupriferous Prussian blue (ACPA) is established as a metal-based theranostic nanoagent for a chronic nonhealing diabetic wound treatment. Prussian blue offers a programmable nanoplatform to formulate metal ion prescriptions, achieving cooperative wound healing. Silver, copper, and iron ions are released from ACPA controlled by the near-infrared-triggered mild hyperthermia and then synergistically participate in antipathogen, cell migration, and revascularization. ACPA also demonstrates a unique core-satellite nanostructure which enables it with improved surface-enhanced Raman scattering (SERS) capability as potent bacteria-targeted Raman-silent nanoprobe to monitor the residual bacteria during wound healing with nearly zero background. The theranostic feature of ACPA allows high-performance SERS imaging-guided chronic wound healing in infectious diabetic skin and keratitis.
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Systemic lupus erythematosus (SLE) is a chronic autoimmune disease which leads to the formation of immune complex deposits in multiple organs and has heterogeneous clinical manifestations. Currently, exosomes for liquid biopsy have been applied in diagnosis and monitoring of diseases, whereas SLE discrimination based on exosomes at the metabolic level is rarely reported. Herein, we constructed a protocol for metabolomic study of urinary exosomes from SLE patients and healthy controls (HCs) with high efficiency and throughput. Exosomes were first obtained by high-performance liquid size-exclusion chromatography (HPL-SEC), and then metabolic fingerprints of urinary exosomes were extracted by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) with high throughput and high efficency. With the statistical analysis by orthogonal partial least-squares discriminant analysis (OPLS-DA) model, SLE patients were efficiently distinguished from HCs, the area under the curve (AUC) of the receiver characteristic curve (ROC) was 1.00, and the accuracy of the unsupervised clustering heatmap was 90.32%. In addition, potential biomarkers and related metabolic pathways were analyzed. This method, with the characteristics of high throughput, high efficiency, and high accuracy, will provide the broad prospect of exosome-driven precision medicine and large-scale screening in clinical applications.
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Beauveria bassiana acts as an endophytic fungus that controls herbivorous pests by stimulating plant defenses and inducing systemic resistance. Through multiomics analysis, 325 differential metabolites and 1739 differential expressed genes were observed in tomatoes treated with B. bassiana by root irrigation; meanwhile, 152 differential metabolites and 1002 differential genes were observed in tomatoes treated by local leaf spraying. Among the upregulated metabolites were α-solanine, 5-O-caffeoylshikimic acid, clerodendrin A, and peucedanin, which demonstrated anti-insect activity. These differential metabolites were primarily associated with alkaloid biosynthesis, flavonoid biosynthesis, and tryptophan metabolism pathways. Furthermore, the gene silencing of UDP-glucose:sterol glucosyltransferase, a gene involved in α-solanine synthesis, indicated that B. bassiana could inhibit the reproduction of whiteflies by regulating α-solanine. This study highlighted the ability of B. bassiana to modulate plant secondary metabolites and emphasized the significance of understanding and harnessing multitrophic interactions of endophytic B. bassiana for sustainable agriculture.