A Monte Carlo Simulation Dataset of Radiation Field Parameters for a Simple Nuclear Facility Scenario.

Radioactive safety in nuclear facilities is of utmost importance. Prior to workers entering these areas, a 3D radiation field is needed for accurately estimating their exposure. Due to the complex relationship between radiation measurements and radiation fields, implementing neural networks is a promising approach for reconstruction. However, research on direct 3D radiation field reconstruction using neural networks is limited, and there is no standardized open-source dataset for training and evaluation. To address these issues, we created a simplified model of a nuclear facility and utilized the Monte Carlo program MCShield to simulate 3D radiation parameters. MCShield, which is mainly used for shielding calculations, has been verified for accuracy through benchmark tests. In addition, this paper proves the correctness of the MCShield program and the effectiveness of the AIS variance reduction method through calculations on the WinFrith Iron benchmark experiment and the NUREG/CR-6115 benchmark. The results show that the MCShield program as well as the AIS method can be used for dataset calculations.

Prognostic effect of the TyG index on patients with severe aortic stenosis following transcatheter aortic valve replacement: a retrospective cohort study.

BACKGROUND: The triglyceride glucose (TyG) index, as a reliable marker of insulin resistance, is associated with the incidence and poor prognosis of various cardiovascular diseases. However, the relationship between the TyG index and clinical outcomes in patients with severe aortic stenosis (AS) who underwent transcatheter aortic valve replacement (TAVR) remains unclear. METHODS: This study consecutively enrolled 1569 patients with AS underwent TAVR at West China Hospital of Sichuan University between April 2014 and August 2023. The outcomes of interest included all-cause mortality, cardiovascular mortality, and major adverse cardiovascular events (MACE). Multivariate adjusted Cox regression and restricted cubic splines (RCS) regression analyses were used to assess the associations between the TyG index and the clinical outcomes. The incremental prognostic value of the TyG index was further assessed by the time-dependent Harrell's C-index, integrated discrimination improvement (IDI) and the net reclassification improvement (NRI). RESULTS: During a median follow-up of 1.09 years, there were 146, 70, and 196 patients experienced all-cause death, cardiovascular death, and MACE, respectively. After fully adjusting for confounders, a per-unit increase of TyG index was associated with a 441% (adjusted HR: 5.41, 95% CI: 4.01-7.32), 385% (adjusted HR: 4.85, 95% CI: 3.16-7.43), and 347% (adjusted HR: 4.47, 95% CI: 3.42-5.85) higher risk of all-cause mortality, cardiovascular mortality and MACE, respectively. The RCS regression analyses revealed a linear association between TyG index and endpoints (all P for non-linearity > 0.05) with 8.40 as the optimal binary cutoff point. Furthermore, adding TyG index to the basic risk model provided a significant incremental value in predicting poor prognosis (Time-dependent Harrell's C-index increased for all the endpoints; All-cause mortality, IDI: 0.11, P < 0.001; NRI: 0.32, P < 0.001; Cardiovascular mortality, IDI: 0.043, P < 0.001; NRI: 0.37, P < 0.001; MACE, IDI: 0.092, P < 0.001; NRI: 0.32, P < 0.001). CONCLUSIONS: In patients with severe AS receiving TAVR, there was a positive linear relationship between TyG index and poor prognosis, with 8.4 as the optimal bivariate cutoff value. Our findings suggest TyG index holds potential value for risk stratification and guiding therapeutic decisions in patients after TAVR.

Individualized dose calculation for internal exposure on radionuclide intake: GPU acceleration approach.

Objective. The rapid and accurate assessment of internal exposure dose is a crucial safeguard for personnel health and safety. This study aims to investigate a precise and efficient GPU Monte Carlo simulation approach for internal exposure dose calculation. It directly calculates doses from common radioactive nuclides intake, like60Co for occupational exposure, allowing personalized assessments.Approach. This study developed a GPU-accelerated Monte Carlo program for internal exposure on radionuclide intake, successfully realizing photoelectronic coupled transport, nuclide simulation, and optimized acceleration. The generation of internal irradiation sources and sampling methods were achieved, along with the establishment of a personalized phantom construction process. Three irradiation scenarios were simulated to assess computational accuracy and efficiency, and to investigate the influence of posture variations on internal dose estimations.Main results. Using the International Commission on Radiological Protection (ICRP) voxel-type phantom, the internal dose of radionuclides in individual organs was calculated, exhibiting relative deviation of less than 3% in comparison to organ dose results interpolated by Specific Absorbed Fractions in ICRP Publication 133. Employing the Chinese reference phantom for calculating internal irradiation dose from the intake of various radionuclides, the use of GPU Monte Carlo program significantly shortened the simulation time compared to using CPU programs, by a factor of 150-500. Internal dose estimation utilizing a seated Chinese phantom revealed up to a 75% maximum difference in organ dose compared to the same phantom in a standing posture.Significance. This study presents a rapid GPU-based simulation method for internal irradiation doses, capable of directly simulating dose outcomes from nuclide intake and accommodating individualized phantoms for more realistic and expeditious calculations tailored to specific internal irradiation scenarios. It provides an effective and feasible tool for precisely calculating internal irradiation doses in real-world scenarios.

A Critical Candidate Node-Based Attack Model of Network Controllability.

The controllability of complex networks is a core issue in network research. Assessing the controllability robustness of networks under destructive attacks holds significant practical importance. This paper studies the controllability of networks from the perspective of malicious attacks. A novel attack model is proposed to evaluate and challenge network controllability. This method disrupts network controllability with high precision by identifying and targeting critical candidate nodes. The model is compared with traditional attack methods, including degree-based, betweenness-based, closeness-based, pagerank-based, and hierarchical attacks. Results show that the model outperforms these methods in both disruption effectiveness and computational efficiency. Extensive experiments on both synthetic and real-world networks validate the superior performance of this approach. This study provides valuable insights for identifying key nodes crucial for maintaining network controllability. It also offers a solid framework for enhancing network resilience against malicious attacks.

Mathematical analysis of FLASH effect models based on theoretical hypotheses.

OBJECTIVE: Clinical applications of FLASH radiotherapy require formulas to describe how the FLASH radiation features and other related factors determine the FLASH effect. Mathematical analysis of the models can connect the theoretical hypotheses with the radiobiological effect, which provides the foundation for establishing clinical application models. Moreover, experimental and clinical data can be used to explore the key factors through mathematical analysis. Approach: We abstract the complex models of the oxygen depletion hypothesis and radical recombination-antioxidants hypothesis into concise mathematical equations. The equations are solved to analyze how the radiation features and other factors influence the FLASH effect. Then we propose methodologies for determining the parameters in the models and utilizing the models to predict the FLASH effect. Main results: The formulas linking the physical, chemical and biological factors to the FLASH effect are obtained through mathematical derivation of the equation. The analysis indicates that the initial oxygen concentration, radiolytic oxygen consumption and oxygen recovery are key factors for the oxygen depletion hypothesis and that the level of antioxidants is the key factor for the radical recombination-antioxidants hypothesis. According to the model derivations and analysis, the methodologies for determining parameters and predicting the FLASH effect are proposed: the criteria for data filtration; the strategy of hybrid FLASH and conventional dose rate (CONV) irradiation to ensure the acquisition of effective experimental data across a wide dose range; pipelines of fitting parameters and predicting the FLASH effect. Significance: This study establishes the quantitative relationship between the FLASH effect and key factors. The derived formulas can be used to calculate the FLASH effect in future clinical FLASH radiotherapy. The proposed methodologies guide to obtain sufficient high-quality datasets and utilize them to predict FLASH effect. Furthermore, this study indicates the key factors of FLASH effect and offers clues to further explore the FLASH mechanism.

Enhanced Immune Responses in Mice by Combining the Mpox Virus B6R-Protein and Aluminum Hydroxide-CpG Vaccine Adjuvants.

Novel adjuvants and innovative combinations of adjuvants (Adjuvant Systems) have facilitated the development of enhanced and new vaccines against re-emerging and challenging pathogenic microorganisms. Nonetheless, the efficacy of adjuvants is influenced by various factors, and the same adjuvant may generate entirely different immune responses when paired with different antigens. Herein, we combined the MPXV-B6R antigen with BC02, a novel adjuvant with proprietary technology, to assess its capability to induce both cellular and humoral immunity in mouse models. Mice received two intramuscular injections of B6R-BC02, which resulted in the production of MPXV-specific IgG, IgG1, and IgG2a antibodies. Additionally, it elicited strong MPXV-specific Th1-oriented cellular immunity and persistent effector memory B-cell responses. The advantages of BC02 were further validated, including rapid initiation of the immune response, robust recall memory, and sustained immune response induction. Although the potential of immunized mice to produce serum-neutralizing antibodies against the vaccinia virus requires further improvement, the exceptional performance of BC02 as an adjuvant for the MPXV-B6R antigen has been consistently demonstrated.

A Multitask Network Robustness Analysis System Based on the Graph Isomorphism Network.

Despite various measures across different engineering and social systems, network robustness remains crucial for resisting random faults and malicious attacks. In this study, robustness refers to the ability of a network to maintain its functionality after a part of the network has failed. Existing methods assess network robustness using attack simulations, spectral measures, or deep neural networks (DNNs), which return a single metric as a result. Evaluating network robustness is technically challenging, while evaluating a single metric is practically insufficient. This article proposes a multitask analysis system based on the graph isomorphism network (GIN) model, abbreviated as GIN-MAS. First, a destruction-based robustness metric is formulated using the destruction threshold of the examined network. A multitask learning approach is taken to learn the network robustness metrics, including connectivity robustness, controllability robustness, destruction threshold, and the maximum number of connected components. Then, a five-layer GIN is constructed for evaluating the aforementioned four robustness metrics simultaneously. Finally, extensive experimental studies reveal that 1) GIN-MAS outperforms nine other methods, including three state-of-the-art convolutional neural network (CNN)-based robustness evaluators, with lower prediction errors for both known and unknown datasets from various directed and undirected, synthetic, and real-world networks; 2) the multitask learning scheme is not only capable of handling multiple tasks simultaneously but more importantly it enables the parameter and knowledge sharing across tasks, thus preventing overfitting and enhancing the performances; and 3) GIN-MAS performs multitasks significantly faster than other single-task evaluators. The excellent performance of GIN-MAS suggests that more powerful DNNs have great potentials for analyzing more complicated and comprehensive robustness evaluation tasks.

Circadian metabolites for evaluating the timing of bloodstain deposition: A preliminary study.

Metabolites, as products of cellular metabolism, can provide a wealth of biological information and are less susceptible to degradation than other biomarkers due to their low molecular weight. Due to these properties, metabolites can be used as valuable biomarkers for forensic investigations. Knowing the timing of deposition of bloodstain could help to reconstruct crime scenes, draw conclusions about the time of the crime, and narrow down the circle of possible suspects. Previous studies have indicated that the concentration of some metabolites in blood is subject to circadian changes. However, the circadian metabolites of bloodstains have been still unclear. A total of sixty-four bloodstain samples were prepared under real conditions in three time categories (morning/noon (09:00 h ∼ 17:00 h), afternoon/evening (18:00 h ∼ 23:00 h) and night/early morning (24:00 h ∼ 08:00 h)). Fifty metabolites of bloodstains with significant differences were identified in the three time categories. Twenty-eight of these metabolites exhibited significant circadian changes. Finally, three independently contributing circadian metabolites were selected to build the logistic regression model, with an area under the curve of 0.91, 0.84 and 0.87 for the prediction of bloodstain deposition time in the morning/noon, afternoon/evening and night/early morning, respectively. The study indicated that circadian metabolites can be used for evaluating the timing of bloodstain deposition. This would provide a valuable perspective for analyzing the deposition time of biological traces in forensic investigations.

A lipid activated color switchable probe for the imaging of diseased aortic valves.

Lipid deposition has been considered one of the key factors in the occurrence of valvular heart disease (VHD) and a great potential target for the diagnosis of VHD. However, the development of lipid imaging technologies and efficient lipid specific probes is in urgent demand. In this work, we have prepared a lipid droplets (LDs) targeted fluorescence probe CPTM based on a push-pull electronic structure for the imaging of diseased aortic valves. CPTM showed obvious twisted intramolecular charge transfer (TICT) effect and its emission changed from 600 nm in water to 508 nm in oil. CPTM not only exhibited good biocompatibility and high photostability, but also impressive LDs specific imaging performance in human primary valvular interstitial cells and human diseased aortic valves. Moreover, the dynamic changes of intracellular LDs could be monitor in real-time after staining with CPTM. These results were expected to offer new ideals for the designing of novel LDs specific probes for further bioimaging applications.

Male-specific age prediction based on Y-chromosome DNA methylation with blood using pyrosequencing.

Age prediction is an important aspect of forensic science that offers valuable insight into identification. In recent years, extensive studies have been conducted on age prediction based on DNA methylation, and numerous studies have demonstrated that DNA methylation is a reliable biomarker for age prediction. However, almost all studies on age prediction based on DNA methylation have focused on age-related CpG sites in autosomes, which are concentrated on single-source DNA samples. Mixed samples, especially male-female mixed samples, are common in forensic casework. The application of Y-STRs and Y-SNPs can provide clues for the genetic typing of male individuals in male-female mixtures, but they cannot provide the age information of male individuals. Studies on Y-chromosome DNA methylation can address this issue. In this study, we identified five age-related CpG sites on the Y chromosome (Y-CpGs) and developed a male-specific age prediction model using pyrosequencing combined with a support vector machine algorithm. The mean absolute deviation of the model was 5.50 years in the training set and 6.74 years in the testing set. When we used a male blood sample to predict age, the deviation between the predicted and chronological age was 1.18 years. Then, we mixed the genomic DNA of the male and a female at ratios of 1:1, 1:5, 1:10, and 1:50, the range of deviation between the predicted and chronological age of the male in the mixture was 1.16-1.74 years. In addition, there was no significant difference between the methylation values of bloodstains and blood in the same sample, which indicates that our model is also suitable for bloodstain samples. Overall, our results show that age prediction using DNA methylation of the Y chromosome has potential applications in forensic science and can be of great help in predicting the age of males in male-female mixtures. Furthermore, this work lays the foundation for future research on age-related applications of Y-CpGs.

Absence of PD-L1 signaling hinders macrophage defense against Mycobacterium tuberculosis via upregulating STAT3/IL-6 pathway.

The blockade of programmed death-ligand 1 (PD-L1) pathway has been clinically used in cancer immunotherapy, while its effects on infectious diseases remain elusive. Roles of PD-L1 signaling in the macrophage-mediated innate immune defense against M.tb is unclear. In this study, the outcomes of tuberculosis (TB) in wild-type (WT) mice treated with anti-PD-1/PD-L1 therapy and macrophage-specific Pdl1-knockout (Pdl1ΔΜΦ) mice were compared. Treatment with anti-PD-L1 or anti-PD-1 benefited protection against M.tb infection in WT mice, while Pdl1ΔΜΦ mice exhibited the increased susceptibility to M.tb infection. Mechanistically, the absence of PD-L1 signaling impaired M.tb killing by macrophages. Furthermore, elevated STAT3 activation was found in PD-L1-deficient macrophages, leading to increased interleukin (IL)-6 production and reduced inducible nitric oxide synthase (iNOS) expression. Inhibiting STAT3 phosphorylation partially impeded the increase in IL-6 production and restored iNOS expression in these PD-L1-deficient cells. These findings provide valuable insights into the complexity and mechanisms underlying anti-PD-L1 therapy in the context of tuberculosis.

Visual identification for species and sex derived from bloodstain based on phosphate-mediated isothermal amplification colorimetric system.

Species and sex confirmation of the biological specimen play a crucial role in crime investigation. However, the specimen found in the scene is always trace quantity, which is hard to be analyzed by current methods. Moreover, the time-consuming DNA extraction, sophisticated apparatus, and complex data processing make it difficult to satisfy the demand of speediness and convenience for point-of-care tests. In this study, we first exhibit a phosphate-based visual system for field-based species and sex identification derived from trace bloodstain. By introducing phosphate ion-based colorimetry into loop-mediated isothermal amplification (LAMP) for result interpretation, not only the bloodstain can be directly submitted to mitochondrial variant amplification owing to the enhanced amplification efficiency by pyrophosphate ion hydrolyzation, but also the colorimetric signal can be recognized by the naked eye for result output within 30 min through molybdophosphate generation. Aerosol contamination, the major conflict of LAMP, has been solved once and for all by integrating uracil-DNA glycosylase into this system that still holds on a constant temperature. As a demonstration, cytochrome b and Y-chromosomal amelogenin are employed to identify species and sex respectively, which has achieved a highly sensitive and specific distinguishability under a strong interferential background. Accurate results can be obtained from both the simulative degraded and dated specimen, which indicates that this novel system may serve as a promising tool in forensic practice.

Evaluation of reverse transcription yield of RNA standards and forensic samples based on droplet digital PCR.

RNA analysis has shown great value in forensic science, such as body fluids and tissue identification, postmortem interval estimation, biological age prediction, etc. Currently, most RNA follow-up experiments involve reverse transcription (RT) procedures. It has been shown that the RT step is variable and has a greater impact on subsequent data analysis, especially for forensic trace samples. However, the pattern of variation between different RNA template inputs and complementary DNA (cDNA) yield is unclear. In this study, a series of 2-fold gradient dilutions of RNA standards (1 µg/µL - 0.24 ng/µL) and forensic samples (including blood samples, saliva samples, bloodstains, and saliva stains) were reverse-transcribed using EasyQuick RT MasterMix. The obtained cDNA was quantified by droplet digital PCR (ddPCR) to assess the RT yield of the ACTB gene. The results showed that the 125 ng RNA template had the highest RT yield in a 10 µL RT reaction system with the selected kit. For all stain samples, the RT yield improved as the amount of RNA template input increased since RNA quantities were below 125 ng. As many commercialized reverse transcription kits using different kinds of enzymes are available for forensic RNA research, we recommend that systematic experiments should be performed in advance to determine the amount of RNA input at the optimum RT yield when using any kit for reverse transcription experiments.

CNN-HT: A Two-Stage Algorithm Selection Framework.

The No Free Lunch Theorem tells us that no algorithm can beat other algorithms on all types of problems. The algorithm selection structure is proposed to select the most suitable algorithm from a set of algorithms for an unknown optimization problem. This paper introduces an innovative algorithm selection approach called the CNN-HT, which is a two-stage algorithm selection framework. In the first stage, a Convolutional Neural Network (CNN) is employed to classify problems. In the second stage, the Hypothesis Testing (HT) technique is used to suggest the best-performing algorithm based on the statistical analysis of the performance metric of algorithms that address various problem categories. The two-stage approach can adapt to different algorithm combinations without the need to retrain the entire model, and modifications can be made in the second stage only, which is an improvement of one-stage approaches. To provide a more general structure for the classification model, we adopt Exploratory Landscape Analysis (ELA) features of the problem as input and utilize feature selection techniques to reduce the redundant ones. In problem classification, the average accuracy of classifying problems using CNN is 96%, which demonstrates the advantages of CNN compared to Random Forest and Support Vector Machines. After feature selection, the accuracy increases to 98.8%, further improving the classification performance while reducing the computational cost. This demonstrates the effectiveness of the first stage of the CNN-HT method, which provides a basis for algorithm selection. In the experiments, CNN-HT shows the advantages of the second stage algorithm as well as good performance with better average rankings in different algorithm combinations compared to the individual algorithms and another algorithm combination approach.

Body fluids should be identified before estimating the time since deposition (TsD) in microbiome-based stain analyses for forensics.

Identification and the time since deposition (TsD) estimation of body fluid stains from a crime scene could provide valuable information for solving the cases and are always difficult for forensics. Microbial characteristics were considered as a promising biomarker to address the issues. However, changes in the microbiota may damage the specific characteristics of body fluids. Correspondingly, incorrect body fluid identification may result in inaccurate TsD estimation. The mutual influence is not well understood and limited the codetection. In the current study, saliva, semen, vaginal secretion, and menstrual blood samples were exposed to indoor conditions and collected at eight time points (from fresh to 30 days). High-throughput sequencing based on the 16S rRNA gene was performed to characterize the microbial communities. The results showed that a longer TsD could decrease the discrimination of different body fluid stains. However, the accuracies of identification still reached a quite high value even without knowing the TsD. Correspondingly, the mean absolute error (MAE) of TsD estimation significantly increased without distinguishing the types of body fluids. The predictive TsD of menstrual blood reached a quite low MAE (1.54 ± 0.39 d). In comparison, those of saliva (6.57 ± 1.17 d), semen (6.48 ± 1.33 d), and vaginal secretion (5.35 ± 1.11 d) needed to be further improved. The great effect of individual differences on these stains limited the TsD estimation accuracy. Overall, microbial characteristics allow for codetection of body fluid identification and TsD estimation, and body fluids should be identified before estimating TsD in microbiome-based stain analyses.IMPORTANCEEmerged evidences suggest microbial characteristics could be considered a promising tool for identification and time since deposition (TsD) estimation of body fluid stains. However, the two issues should be studied together due to a potential mutual influence. The current study provides the first evidence to understand the mutual influence and determines an optimal process for codetection of identification and TsD estimation for unknown stains for forensics. In addition, we involved aged stains into our study for identification of body fluid stains, rather than only using fresh stains like previous studies. This increased the predictive accuracy. We have preliminary verified that individual differences in microbiotas limited the predictive accuracy of TsD estimation for saliva, semen, and vaginal secretion. Microbial characteristics could provide an accurate TsD estimation for menstrual blood. Our study benefits the comprehensive understanding of microbiome-based stain analyses as an essential addition to previous studies.

THUBreast: an open-source breast phantom generation software for x-ray imaging and dosimetry.

Objective. Establishing realistic phantoms of human anatomy is a continuing concern within virtual clinical trials of breast x-ray imaging. However, little attention has been paid to glandular distribution within these phantoms. The principal objective of this study was to develop breast phantoms considering the clinical glandular distribution.Approach. This research introduces an innovative method for integrating glandular distribution information into breast phantoms. We have developed an open-source software, THUBreast44http://github.com/true02Hydrogen/THUBreast/, which generates breast phantoms that accurately replicate both the structural texture and glandular distribution, two crucial elements in breast x-ray imaging and dosimetry. To validate the efficacy of THUBreast, we assembled three groups of breast phantoms (THUBreast, patient-based, homogeneous) for irradiation simulation and calculated the power-law exponents (ß) and mean glandular dose (Dg), indicators of texture realism and radiation risk, respectively, utilizing MC-GPU.Main results. Upon the computation of theDgfor the THUBreast phantoms, it was found to be in agreement with that absorbed by the phantoms based on patients, with an average deviation of 4%. The estimates of averageDgthus obtained were on average 23% less than those computed for the homogeneous phantoms. It was observed that the homogeneous phantoms did overestimate the averageDgby 30% when compared to the phantoms based on patients. The mean value ofßfor the images of THUBreast phantoms was found to be 2.92 ± 0.08, which shows a commendable agreement with the findings of prior investigations.Significance. It is evidently clear from the results that THUBreast phantoms have a preliminary good performance in both imaging and dosimetry in terms of indicators of texture realism and glandular dose. THUBreast represents a further step towards developing a powerful toolkit for comprehensive evaluation of image quality and radiation risk.

Computational model of radiation oxygen effect with Monte Carlo simulation: effects of antioxidants and peroxyl radicals.

PURPOSE: Oxygen plays a crucial role in radiation biology. Antioxidants and peroxyl radicals affect the oxygen effect greatly. This study aims to establish a computational model of the oxygen effect and explore the effect attributed to antioxidants and peroxyl radicals. MATERIALS AND METHODS: Oxygen-related reactions are added to our track-structure Monte Carlo code NASIC, including oxygen fixation, chemical repair by antioxidants and damage migration from base-derived peroxyl radicals. Then the code is used to simulate the DNA damage under various oxygen, antioxidant and damage migration rate conditions. The oxygen enhancement ratio(OER) is calculated quantifying by the number of double-strand breaks for each condition. The roles of antioxidants and peroxyl radicals are examined by manipulating the relevant parameters. RESULTS AND CONCLUSIONS: Our results indicate that antioxidants are capable of rapidly restoring DNA radicals through chemical reactions, which compete with natural and oxygen fixation processes. Additionally, antioxidants can react with peroxyl radicals derived from bases, thereby preventing the damage from migrating to DNA strands. By quantitatively accounting for the impact of peroxyl radicals and antioxidants on the OER curves, our study establishes a more precise and comprehensive model of the radiation oxygen effect.

Intracellular and extracellular synergistic therapy for restoring macrophage functions via anti-CD47 antibody-conjugated bifunctional nanoparticles in atherosclerosis.

Atherosclerosis is a significant contributor to global cardiovascular disease. Reducing the formation of atherosclerotic plaque effectively can lead to a decrease in cardiovascular diseases. Therefore, controlling macrophage function is crucial. This study presents the creation of a bifunctional nanoparticle that is specific to macrophages to achieve intracellular and extracellular synergistic therapy for restoring macrophage functions. The nanoparticle is conjugated with anti-CD47 antibody to modulate extracellular CD47-SIRPα phagocytic signaling axis on the outer surface of macrophages and encapsulates the NLRP3 inhibitor (CY-09) to regulate intracellular inflammation response of macrophages. The results showed that the nanoparticles accumulate in the atherosclerotic plaque, alter macrophage phagocytosis, inhibit NLRP3 inflammasome activation, and decrease the plaque burden in Apoe-/- mice whilst ensuring safety. Examination of single-cell RNA sequencing indicates that this multifunctional nanoparticle decreases the expression of genes linked to inflammation and manages inflammatory pathways in the plaque lesion. This study proposes a synergistic therapeutic approach that utilizes a bifunctional nanoparticle, conjugated with anti-CD47, to regulate the microenvironment of plaques.

TEMPO-Mediated Interrupted 6π-Photocyclization of ortho-Biaryl-Appended 1,3-Dicarbonyl Compounds toward 10-Phenanthrenols.

In this paper, we present an example of a photoinduced catalyst, halogen-, and base-free TEMPO-mediated interrupted 6π-photocyclization/dehydrogenative aromatization of ortho-biaryl-appended 1,3-dicarbonyl compounds for the preparation of 10-phenanthrenols. The reaction involves rapid photocycloaddition via a 1,2-biradical of 1,3-dicarbonyl compounds, followed by subsequent dehydrogenative aromatization of 1,4-biradical intermediates using TEMPO as the commercially available oxidant rather than trapped by TEMPO to form an alkoxyamine product.

A Comparative Study on the Mechanism of Delayed-Type Hypersensitivity Mediated by the Recombinant Mycobacterium tuberculosis Fusion Protein ESAT6-CFP10 and Purified Protein Derivative.

While purified protein derivative (PPD) is commonly used as skin diagnostic reagent for tuberculosis (TB) infection, it cannot distinguish effectively Bacillus Calmette-Guérin (BCG) vaccination from Mycobacterium tuberculosis (MTB) complex and nontuberculous mycobacteria infection. The new skin reagent ESAT6-CFP10 (EC) has favorable sensitivity and specificity, which can overcome limitations associated with PPD. At present, EC skin test reactions are mainly characterized by erythema, while PPD mainly causes induration. We conducted a comparative study on the potential differences between EC-induced erythema and PPD-induced induration using a guinea pig model. The size of EC-dependent erythema was similar to that of PPD-induced induration, and an inflammatory response characterized by the infiltration of monocytes, macrophages and lymphocytes, as well as tissue damage, appeared at the injection site. The lymphocytes included CD4+ T and CD8+ T cells, which released IFN-γ as the main cytokine. Both EC erythema and PPD induration could lead to increased levels of acute-phase proteins, and the differential pathways were similar, thus indicating that the main induced immune pathways were similar. The above results indicated that erythema produced by EC could generate the main delayed-type hypersensitivity (DTH) response characteristic of PPD induration, thereby suggesting that erythema might also have a certain diagnostic significance and provide a possible theoretical basis for its use as a diagnostic indicator for detecting MTB infection.