High-temperature PTT/CDT coordination nanoplatform realizing exacerbated hypoxia for enhancing hypoxia-activated chemotherapy to overcome tumor drug resistance.

BACKGROUND: Hypoxia-activated prodrugs present new opportunities for safe and effective tumor drug resistance therapy due to their high selectivity for hypoxic cells. However, the uneven distribution of oxygen in solid tumor and insufficient hypoxia in the tumor microenvironment greatly limit its therapeutic efficacy. RESULTS: In this paper, a novel AQ4N-Mn(II)@PDA coordination nanoplatform was designed and functionalized with GMBP1 to target drug-resistant tumor cells. Its excellent photothermal conversion efficiency could achieve local high-temperature photothermal therapy in tumors, which could not only effectively exacerbate tumor hypoxia and thus improve the efficacy of hypoxia-activated chemotherapy of AQ4N but also significantly accelerate Mn2+-mediated Fenton-like activity to enhance chemodynamic therapy. Moreover, real-time monitoring of blood oxygen saturation through photoacoustic imaging could reflect the hypoxic status of tumors during treatment. Furthermore, synergistic treatment effectively inhibited tumor growth and improved the survival rate of mice bearing orthotopic drug-resistant tumors. CONCLUSIONS: This study not only provided a new idea for PTT combined with hypoxia-activated chemotherapy and CDT for drug-resistant tumors but also explored a vital theory for real-time monitoring of hypoxia during treatment.

Folic acid-mediated hollow Mn 3 O 4 nanocomposites for in vivo MRI/FLI monitoring the metastasis of gastric cancer.

BACKGROUND: Metastasis is one of the main factors leading to the high mortality rate of gastric cancer. The current monitoring methods are not able to accurately monitor gastric cancer metastasis. METHODS: In this paper, we constructed a new type of hollow Mn 3 O 4 nanocomposites, Mn 3 O 4 @HMSN-Cy7.5-FA, which had a size distribution of approximately 100 nm and showed good stability in different liquid environments. The in vitro magnetic resonance imaging (MRI) results show that the nanocomposite has good response effects to the acidic microenvironment of tumors. The acidic environment can significantly enhance the contrast of T 1 -weighted MRI. The cellular uptake and endocytosis results show that the nanocomposite has good targeting capabilities and exhibits good biosafety, both in vivo and in vitro. In a gastric cancer nude mouse orthotopic metastatic tumor model, with bioluminescence imaging's tumor location information, we realized in vivo MRI/fluorescence imaging (FLI) guided precise monitoring of the gastric cancer orthotopic and metastatic tumors with this nanocomposite. RESULTS: This report demonstrates that Mn 3 O 4 @HMSN-Cy7.5-FA nanocomposites is a promising nano-diagnostic platform for the precision diagnosis and therapy of gastric cancer metastasis in the future. CONCLUSIONS: In vivo MRI/FLI imaging results show that the nanocomposites can achieve accurate monitoring of gastric cancer tumors in situ and metastases. BLI's tumor location information further supports the good accuracy of MRI/FLI dual-modality imaging. The above results show that the MHCF NPs can serve as a good nano-diagnostic platform for precise in vivo monitoring of tumor metastasis. This nanocomposite provides more possibilities for the diagnosis and therapy of gastric cancer metastases.

Injectable temperature-sensitive hydrogel facilitating endoscopic submucosal dissection.

Purpose: Early gastrointestinal tumors can be removed by endoscopic procedures. Endoscopic mucosal dissection (ESD) requires submucosal fluid injection to provide mucosal elevation and prevent intraoperative perforation. However, the clinically applied normal saline mucosal elevation height is low for a short time, which often requires multiple intraoperative injections that increase the inconvenience and procedure time. In addition, recently researched submucosal injection materials (SIM) suffer from complex preparation, poor economy, and poor biocompatibility. Therefore, there is an urgent need for a new type of SIM that can provide long, safe and effective mucosal elevation in support of the endoscopic procedures. Methods: The FS hydrogel is based on polyethylene-polypropylene glycol (F-127) mixed with sodium alginate (SA). The different physicochemical properties of FS hydrogels were characterized through various experiments. Afterward, various biosafety assessments were carried out. Finally, the performance of FS hydrogels was evaluated by in vitro submucosal injection and in vivo swine ESD. Results: The experimental results show that the FS hydrogel is liquid at room temperature, making it easy to inject, and when injected under the mucosa, it undergoes temperature-induced cross-linking, transforming from a liquid to a solid state to provide long-lasting mucosal augmentation. At the same time, the FS hydrogel exhibits controllable gelation, stability, and biocompatibility. The results of in vitro submucosal injections and in vivo ESD procedures showed that FS achieves high mucosal augmentation and provides good submucosal cushioning in the long term. Conclusion: In summary, the F-127/SA hydrogel is simple to synthesize, cost-effective, safe, easy to store, and able to assist ESD well from the perspective of practical clinical problems, indicating that the FS hydrogel can be an ideal potent submucosal injection substitution.

Exploring Radiomics Features Based on H&E Images as Potential Biomarkers for Evaluating Muscle Atrophy: A Preliminary Study.

Radiomics features have been widely used as novel biomarkers in the diagnosis of various diseases, but whether radiomics features derived from hematoxylin and eosin (H&E) images can evaluate muscle atrophy has not been studied. Therefore, this study aims to establish a new biomarker based on H&E images using radiomics methods to quantitatively analyze H&E images, which is crucial for improving the accuracy of muscle atrophy assessment. Firstly, a weightless muscle atrophy model was established by laying macaques in bed, and H&E images of the shank muscle fibers of the control and bed rest (BR) macaques were collected. Muscle fibers were accurately segmented by designing a semi-supervised segmentation framework based on contrastive learning. Then, 77 radiomics features were extracted from the segmented muscle fibers, and a stable subset of features was selected through the LASSO method. Finally, the correlation between radiomics features and muscle atrophy was analyzed using a support vector machine (SVM) classifier. The semi-supervised segmentation results show that the proposed method had an average Spearman's and intra-class correlation coefficient (ICC) of 88% and 86% compared to manually extracted features, respectively. Radiomics analysis showed that the AUC of the muscle atrophy evaluation model based on H&E images was 96.87%. For individual features, GLSZM_SZE outperformed other features in terms of AUC (91.5%) and ACC (84.4%). In summary, the feature extraction based on the semi-supervised segmentation method is feasible and reliable for subsequent radiomics research. Texture features have greater advantages in evaluating muscle atrophy compared to other features. This study provides important biomarkers for accurate diagnosis of muscle atrophy.

Dynamic Privacy-Preserving Anonymous Authentication Scheme for Condition-Matching in Fog-Cloud-Based VANETs.

Secure group communication in Vehicle Ad hoc Networks (VANETs) over open channels remains a challenging task. To enable secure group communications with conditional privacy, it is necessary to establish a secure session using Authenticated Key Agreement (AKA). However, existing AKAs suffer from problems such as cross-domain dynamic group session key negotiation and heavy computational burdens on the Trusted Authority (TA) and vehicles. To address these challenges, we propose a dynamic privacy-preserving anonymous authentication scheme for condition matching in fog-cloud-based VANETs. The scheme employs general Elliptic Curve Cryptosystem (ECC) technology and fog-cloud computing methods to decrease computational overhead for On-Board Units (OBUs) and supports multiple TAs for improved service quality and robustness. Furthermore, certificateless technology alleviates TAs of key management burdens. The security analysis indicates that our solution satisfies the communication security and privacy requirements. Experimental simulations verify that our method achieves optimal overall performance with lower computational costs and smaller communication overhead compared to state-of-the-art solutions.

Contrastive Disentanglement for Quantitative Ultrasound Muscle Atrophy Evaluation.

OBJECTIVE: Muscle atrophy reduces the quality of life and increases morbidity and mortality from other diseases. The development of non-invasive muscle atrophy evaluation method is of great practical value. The lack of gold standard for pathological grading usually allows only the duration of weightlessness as a criterion for the degree of atrophy. However, the adaptive reductive remodeling of muscle physiology and structure shows a trend of nonlinear changes in time. Consequently, using weightlessness time as a benchmark for the degree of atrophy is inaccurate. METHODS: This paper proposes a new ultrasound imaging-based method for quantifying muscle atrophy that utilizes weakly supervised information between multiple data partitions with controlled variance components, overcoming the limitations of using the weightlessness time as a criterion. We introduce a group-supervised contrastive disentanglement network (GCDNet) to disentangle the individual variances, muscle growth and atrophy components of ultrasound images, and quantify the degree of atrophy using the disentangled atrophy component. RESULTS: The feasibility of GCDNet is validated by the separability, independence, and representativeness of the disentangled components. To simplify the application of GCDNet, a muscle atrophy scoring network requiring no reference images is developed by distilling the GCDNet's knowledge of muscle atrophy quantization. The strength of the proposed methodology allows us, for the first time to our knowledge, to study the muscle growth attribute during hind-limb unloading and the spatial distribution of muscle atrophy.

PriTKT: A Blockchain-Enhanced Privacy-Preserving Electronic Ticket System for IoT Devices.

Electronic tickets (e-tickets) are gradually being adopted as a substitute for paper-based tickets to bring convenience to customers, corporations, and governments. However, their adoption faces a number of practical challenges, such as flexibility, privacy, secure storage, and inability to deploy on IoT devices such as smartphones. These concerns motivate the current research on e-ticket systems, which seeks to ensure the unforgeability and authenticity of e-tickets while simultaneously protecting user privacy. Many existing schemes cannot fully satisfy all these requirements. To improve on the current state-of-the-art solutions, this paper constructs a blockchain-enhanced privacy-preserving e-ticket system for IoT devices, dubbed PriTKT, which is based on blockchain, structure-preserving signatures (SPS), unlinkable redactable signatures (URS), and zero-knowledge proofs (ZKP). It supports flexible policy-based ticket purchasing and ensures user unlinkability. According to the data minimization and revealing principle of GDPR, PriTKT empowers users to selectively disclose subsets of (necessary) attributes to sellers as long as the disclosed attributes satisfy ticket purchasing policies. In addition, benefiting from the decentralization and immutability of blockchain, effective detection and efficient tracing of double spending of e-tickets are supported in PriTKT. Considering the impracticality of existing e-tickets schemes with burdensome ZKPs, we replace them with URS/SPS or efficient ZKP to significantly improve the efficiency of ticket issuing and make it suitable for use on smartphones.

Rational design of NIR-II molecule-engineered nanoplatform for preoperative downstaging and imaging-guided surgery of orthotopic hepatic tumor.

Orthotopic advanced hepatic tumor resection without precise location and preoperative downstaging may cause clinical postoperative recurrence and metastasis. Early accurate monitoring and tumor size reduction based on the multifunctional diagnostic-therapeutic integration platform could improve real-time imaging-guided resection efficacy. Here, a Near-Infrared II/Photoacoustic Imaging/Magnetic Resonance Imaging (NIR-II/PAI/MRI) organic nanoplatform IRFEP-FA-DOTA-Gd (IFDG) is developed for integrated diagnosis and treatment of orthotopic hepatic tumor. The IFDG is designed rationally based on the core "S-D-A-D-S" NIR-II probe IRFEP modified with folic acid (FA) for active tumor targeting and Gd-DOTA agent for MR imaging. The IFDG exhibits several advantages, including efficient tumor tissue accumulation, good tumor margin imaging effect, and excellent photothermal conversion effect. Therefore, the IFDG could realize accurate long-term monitoring and photothermal therapy non-invasively of the hepatic tumor to reduce its size. Next, the complete resection of the hepatic tumor in situ lesions could be realized by the intraoperative real-time NIR-II imaging guidance. Notably, the preoperative downstaging strategy is confirmed to lower the postoperative recurrence rate of the liver cancer patients under middle and advanced stage effectively with fewer side effects. Overall, the designed nanoplatform demonstrates great potential as a diagnostic-therapeutic integration platform for precise imaging-guided surgical navigation of orthotopic hepatic tumors with a low recurrence rate after surgery, providing a paradigm for diagnosing and treating the advanced tumors in the future clinical translation application.

MF-Net: Automated Muscle Fiber Segmentation From Immunofluorescence Images Using a Local-Global Feature Fusion Network.

Histological assessment of skeletal muscle slices is very important for the accurate evaluation of weightless muscle atrophy. The accurate identification and segmentation of muscle fiber boundary is an important prerequisite for the evaluation of skeletal muscle fiber atrophy. However, there are many challenges to segment muscle fiber from immunofluorescence images, including the presence of low contrast in fiber boundaries in immunofluorescence images and the influence of background noise. Due to the limitations of traditional convolutional neural network-based segmentation methods in capturing global information, they cannot achieve ideal segmentation results. In this paper, we propose a muscle fiber segmentation network (MF-Net) method for effective segmentation of macaque muscle fibers in immunofluorescence images. The network adopts a dual encoder branch composed of convolutional neural networks and transformer to effectively capture local and global feature information in the immunofluorescence image, highlight foreground features, and suppress irrelevant background noise. In addition, a low-level feature decoder module is proposed to capture more global context information by combining different image scales to supplement the missing detail pixels. In this study, a comprehensive experiment was carried out on the immunofluorescence datasets of six macaques' weightlessness models and compared with the state-of-the-art deep learning model. It is proved from five segmentation indices that the proposed automatic segmentation method can be accurately and effectively applied to muscle fiber segmentation in shank immunofluorescence images.

Indocyanine green-based fluorescence imaging improved by deep learning.

Intraoperative identification of malignancies using indocyanine green (ICG)-based fluorescence imaging could provide real-time guidance for surgeons. Existing ICG-based fluorescence imaging mostly operates in the near-infrared (NIR)-I (700-1000 nm) or the NIR-IIa' windows (1000-1300 nm), which is not optimal in terms of spatial resolution and contrast as their light scattering is higher than the NIR-IIb window (1500-1700 nm). It is highly desired to achieve ICG-based fluorescence imaging in the NIR-IIb window, but it is hindered by its ultra-low NIR-IIb emission tail of ICG. Herein, we employ a generative adversarial network to generate NIR-IIb ICG images directly from the acquired NIR-I ICG images. This approach was investigated by in vivo imaging of sub-surface vascular, intestine structure, and tumors, and their results demonstrated significant improvement in spatial resolution and contrast for ICG-based fluorescence imaging. It is potential for deep learning to improve ICG-based fluorescence imaging in clinical diagnostics and image-guided surgery in clinics.

Detector-trigger-based cardiac multiphase micro-CT imaging for small animals.

BACKGROUND: Micro-computed tomography is important in cardiac imaging for preclinical small animal models, but motion artifacts may appear due to the rapid heart rates. To avoid influence of motion artifacts, the prospective ECG gating schemes based on an X-ray source trigger have been investigated. However, due to the lack of pulsed X-ray exposure modes, high-resolution micro-focus X-ray sources do not support source triggering in most cases. OBJECTIVE: To develop a fast-cardiac multiphase acquisition strategy using prospective ECG gating for micro-focus X-ray tubes with a continuous emission mode. METHODS: The proposed detector-trigger-based prospective ECG gating acquisition scheme (DTB-PG) triggers the X-ray detector at the R peak of ECG, and then collects multiple phase projections of the heart in one ECG cycle by sequence acquisition. Cardiac multiphase images are reconstructed after performing the same acquisition in all views. The feasibility of this strategy was verified in multiphase imaging experiments of a phantom with 150 ms motion period and a mouse heart on a micro-focus micro-CT system with continuous emission mode. RESULTS: Using a high frame-rate CMOS detector, DTB-PG discriminates the positions of the motion phantom well in 10 different phases and enables to distinguish the changes in the cardiac volume of the mouse in different phases. The acquisition rate of DTB-PG is much faster than other prospective gating schemes as demonstrated by theoretical analysis. CONCLUSIONS: DTB-PG combines the advantages of prospective ECG gating strategies and X-ray detector-trigger mode to suppress motion artifacts, achieve ultra-fast acquisition rates, and relax hardware limitations.

Cobalt Ferrite-Gossypol Coordination Nanoagents with High Photothermal Conversion Efficiency Sensitizing Chemotherapy against Bcl-2 to Induce Tumor Apoptosis.

Gossypol is a chemotherapeutic drug that can inhibit the anti-apoptotic protein Bcl-2, but the existing gossypol-related nanocarriers cannot well solve the problem of chemotherapy resistance. Based on the observation that gossypol becomes black upon Fe3+ coordination, it is hypothesized that encasing gossypol in glyceryl monooleate (GMO) and making it coordinate cobalt ferrite will not only improve its photothermal conversion efficiency (PCE) but also help it enter tumor cells. As the drug loading content and drug encapsulation efficiency of gossypol are 10.67% (w/w) and 96.20%, the PCE of cobalt ferrite rises from 14.71% to 36.00%. The synergistic therapeutic effect finally induces tumor apoptosis with a tumor inhibition rate of 96.56%, which is 2.99 and 1.47 times higher than chemotherapy or photothermal therapy (PTT) alone. PTT generated by the GMO nanocarriers under the irradiation of 808 nm laser can weaken tumor hypoxia, thereby assisting gossypol to inhibit Bcl-2. In addition, the efficacy of nanocarriers is also evaluated through T2 -weighted magnetic resonance imaging. Observations of gossypol-induced apoptosis in tissue slices provide definitive proof of chemotherapy sensitization, indicating that such coordination nanocarriers can be used as an effective preclinical agent to enhance chemotherapy.

Cone-beam X-ray luminescence computed tomography based on MLEM with adaptive FISTA initial image.

BACKGROUND AND OBJECTIVE: As an emerging dual-mode optical molecular imaging, cone-beam X-ray luminescence computed tomography (CB-XLCT) has shown potential in early tumor diagnosis and other applications with increased depth and little autofluorescence. However, due to the low transfer efficiency of PNPs to convert X-ray energy to visible or near-infrared (NIR) light and X-ray dose limitation, the signal to noise ratio of projections is quite low, making the quality of CB-XLCT relatively poor. METHODS: To improve the reconstruction quality of low-counts CB-XLCT imaging, an adaptive reconstruction algorithm (named ADFISTA-MLEM) based on the maximum likelihood expectation estimation (MLEM) framework is proposed for CB-XLCT reconstruction from Poisson distributed projections. In the proposed framework, the image reconstructed by fast iterative shrinkage-thresholding algorithm (FISTA) is used as the initial image for MLEM iterations to improve reconstruction accuracy, in which both the projection noise model and the sparsity constraint of the image could be considered. For relative quantitative imaging, a specific normalization is applied to the projection data and system matrix. To determine the hyperparameter of FISTA, which may be different for different projections, an adaptive strategy (ADFISTA) is then designed for adaptive update of the hyperparameter with reconstructed image in each iteration. RESULTS AND CONCLUSIONS: Results from numerical simulations and phantom experiments indicate that the proposed framework can obtain superior reconstruction accuracy in terms of contrast to noise ratio and shape similarity. In addition, high intensity-concentration linearity between different probe targets indicates its potential for quantitative CB-XLCT imaging.

Application of radiomics in precision prediction of diagnosis and treatment of gastric cancer.

Gastric cancer is one of the most common malignant tumors. Although some progress has been made in chemotherapy and surgery, it is still one of the highest mortalities in the world. Therefore, early detection, diagnosis and treatment are very important to improve the prognosis of patients. In recent years, with the proposal of the concept of radiomics, it has been gradually applied to histopathological grading, differential diagnosis, therapeutic efficacy and prognosis evaluation of gastric cancer, whose advantage is to comprehensively quantify the tumor phenotype using a large number of quantitative image features, so as to predict and diagnose the lesion area of gastric cancer early. The purpose of this review is to evaluate the research status and progress of radiomics in gastric cancer, and reviewed the workflow and clinical application of radiomics. The 27 original studies on the application of radiomics in gastric cancer were included from web of science database search results from 2017 to 2021, the number of patients included ranged from 30 to 1680, and the models used were based on the combination of radiomics signature and clinical factors. Most of these studies showed positive results, the median radiomics quality score (RQS) for all studies was 36.1%, and the development prospect and challenges of radiomics development were prospected. In general, radiomics has great potential in improving the early prediction and diagnosis of gastric cancer, and provides an unprecedented opportunity for clinical practice to improve the decision support of gastric cancer treatment at a low cost.

Application of Machine Learning Technology in the Prediction of ADME- Related Pharmacokinetic Parameters.

BACKGROUND: As an important determinant in drug discovery, the accurate analysis and acquisition of pharmacokinetic parameters are very important for the clinical application of drugs. At present, the research and development of new drugs mainly obtain their pharmacokinetic parameters through data analysis, physiological model construction and other methods, but the results are often quite different from the actual situation, needing more manpower and material resources. OBJECTIVE: We mainly discuss the application of machine learning technology in the prediction of pharmacokinetic parameters, which are mainly related to the quantitative study of drug absorption, distribution, metabolism and excretion in the human body, such as bioavailability, clearance, apparent volume of distribution and so on. METHODS: This paper first introduces the pharmacokinetic parameters, the relationship between the quantitative structure-activity relationship model and machine learning, then discusses the application of machine learning technology in different prediction models, and finally discusses the limitations, prospects and future development of the machine learning model in predicting pharmacokinetic parameters. RESULTS: Unlike traditional pharmacokinetic analysis, machine learning technology can use computers and algorithms to speed up the acquisition of pharmacokinetic parameters to varying degrees. It provides a new idea to speed up and shorten the cycle of drug development, and has been successfully applied in drug design and development. CONCLUSION: The use of machine learning technology has great potential in predicting pharmacokinetic parameters. It also provides more choices and opportunities for the design and development of clinical drugs in the future.

Precisely controlled polydopamine-mediated antibacterial system: mathematical model of polymerization, prediction of antibacterial capacity, and promotion of wound healing.

In this work, the polydopamine (PDA)-mediated antibacterial system is synthesized to carry out antimicrobial activities in vitro and in vivo. First, to precisely control the surface modification of nanodiamonds (NDs), a mathematical kinetics model of PDA deposition is established, and the conditions of synthesis reaction are discussed including influencing factors such as the concentrations of dopamine, reaction time, and the kinetic constant k1, which is a function of several variables associated with the reaction temperature, light irradiance (especially at ultraviolet wavelengths), pH value and concentration of dissolved O2 in the solution. A simulated visualization demonstrates that the deposition thickness of PDA is positively correlated with temperature and light irradiance, and PDA is easier to deposit in an alkaline solution and will be terminated if the dissolved O2 is insufficient. Then, the precisely controlled thickness of PDA can control the growth of AgNPs, rendering the intensity of Raman peaks increased and providing a predictable antibacterial effect against E. coli in vitro. An optimized antibacterial hydrogel containing NDs-PDA/Ag is prepared and characterized by the Fourier transform infrared spectroscopy and field emission scanning electron microscopy. Finally, the antibacterial experiments to promote wound healing in vivo are performed, which are verified by pathological and immunohistochemical-stained sections. This work provides a theoretical basis of predicting the PDA-assisted surface modification of NDs, giving a divinable antibacterial effect, and promoting wounds healing in vivo.

Tumor microenvironment responsive Mn3O4 nanoplatform for in vivo real-time monitoring of drug resistance and photothermal/chemodynamic synergistic therapy of gastric cancer.

BACKGROUND: Postoperative chemotherapy for gastric cancer often causes multidrug resistance (MDR), which has serious consequences for therapeutic effects. Individualized treatment based on accurate monitoring of MDR will greatly improve patient survival. RESULTS: In this article, a self-enhanced Mn3O4 nanoplatform (MPG NPs) was established, which can react with glutathione to produce Mn2+ to enhance T1-weighted magnetic resonance imaging (MRI) and mediate in vivo real-time MDR monitoring. In vitro MRI results showed that MRI signals could be enhanced in the presence of hydrogen peroxide and glutathione and at acidic pH. In vivo MRI results indicated that MPG NPs could specifically target MDR cells, thereby realizing real-time monitoring of MDR in gastric cancer. Furthermore, MPG NPs have good chemodynamic activity, which can convert the endogenous hydrogen peroxide of tumor cells into highly toxic hydroxyl radical through Fenton-like reaction at acidic pH to play the role of chemodynamic therapy. In addition, Mn3O4 can significantly enhance the chemodynamic therapy effect because of its good photothermal conversion effect. Furthermore, in situ photothermal/chemodynamic synergistic therapy obtained remarkable results, the tumors of the mice in the synergistic therapy group gradually became smaller or even disappeared. CONCLUSIONS: MPG NPs have good biocompatibility, providing a good nanoplatform for real-time monitoring and precise diagnosis and treatment of MDR in gastric cancer.

Muscle Atrophy Evaluation via Radiomics Analysis Using Ultrasound Images: A Cohort Data Study.

OBJECTIVE: Existing methods for muscle atrophy evaluation based on muscle size measures from ultrasound images are inadequate in precision. Radiomics has been widely used in various medical studies, but its validity for the evaluation of muscle atrophy has not been fully explored. METHODS: This study presents a radiomics analysis for muscle atrophy evaluation using ultrasound images. The hindlimb unloading rat model was developed to simulate weightlessness muscle atrophy and ultrasound images of the hind limbs were acquired for both the hindlimb unloaded (HU) and control groups during a 21-day HU period. A total of 368 radiomics features were extracted and the stable and informative features were selected through a two-stage feature selection procedure. The feature change trajectory of the stable features was analyzed using the hierarchical clustering method. Finally, an adaptive longitudinal feature selection and grading network, ALNet, was developed to evaluate muscle atrophy. RESULTS: The clustering trajectories of ultrasound image features showed similar trends to the changes in muscle atrophy at the molecular level. The best grading accuracy achieved by the ALNet was 79.5% for the Soleus (Sol) muscle and 82.6% for the Gastrocnemius (Gas) muscle. CONCLUSION: The test-retest is essential in performing radiomics analysis on ultrasound images. The longitudinal feature selection is important for muscle atrophy grading. The ultrasound image features of the Gas muscle have better discrimination ability than that of the Sol muscle. This study proves for the first time the capability of ultrasound image features for muscle atrophy evaluation.

DNA Origami-Anthraquinone Hybrid Nanostructures for In Vivo Quantitative Monitoring of the Progression of Tumor Hypoxia Affected by Chemotherapy.

Hypoxia is a well-known feature of malignant solid tumors. To explain the misinterpretation of tumor hypoxia variation during chemotherapy, we developed a DNA origami-based theranostic nanoplatform with an intercalated anticancer anthraquinone as both the chemotherapeutic drug and the photoacoustic contrast agent. The size distribution of the DNA origami nanostructure is 44.5 ± 2.3 nm, whereas the encapsulation efficiency of the drug is 90.7 ± 1.0%, and the drug loading content is 92.2 ± 0.1%. The controlled cumulative release rates were measured in vitro, showing an acidic environment induced rapid drug release. The values of free energy of binding between the drugs and the DNA double helix were calculated through molecular simulations. The cell viability assay was used to characterize cytotoxicity, and fluorescence confocal cell imaging illustrates the biodistribution of the probe in vitro. Photoacoustic and fluorescence imaging were used to indicate drug delivery, release, and biodistribution to predict the drug's chemotherapeutic effect in vivo, whereas the photoacoustic signals were compared with those of deoxygenated/oxygenated hemoglobin to represent the tissue hypoxia/normoxia maps during the chemotherapeutic process and indicate alleviated tumor hypoxia. Staining of tissue sections taken from organs and tumors was used to verify the results of photoacoustic imaging. Our results suggest that photoacoustic imaging can visualize this DNA origami-based theranostic nanoplatform and reveal the mechanisms of chemotherapy on tumor hypoxia.

Biosynthesized Quantum Dots as Improved Biocompatible Tools for Biomedical Applications.

Quantum Dots (QDs), whose diameters are often limited to 10 nm, have been of interest to researchers for their unique optical characteristics, which are attributed to quantum confinement. Following their early application in the electrical industry as light-emitting diode materials, semiconductor nanocrystals have continued to show great potential in clinical diagnosis and biomedical applications. The conventional physical and chemical pathways for QD syntheses typically require harsh conditions and hazardous reagents, and these products encounter non-hydrophilic problems due to organic capping ligands when they enter the physiological environment. The natural reducing abilities of living organisms, especially microbes, are then exploited to prepare QDs from available metal precursors. Low-cost and eco-friendly biosynthesis approaches have the potential for further biomedical applications which benefit from the good biocompatibility of protein-coated QDs. The surface biomass offers many binding sites to modify substances or target ligands, therefore achieving multiple functions through simple and efficient operations. Biosynthetic QDs could function as bioimaging and biolabeling agents because of their luminescence properties similar to those of chemical QDs. In addition, extensive research has been carried out on the antibacterial activity, metal ion detection and bioremediation. As a result, this review details the advanced progress of biomedical applications of biosynthesized QDs and illustrates these principles as clearly as possible.