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Abstract Background Maresin-1 plays an important role in diabetic illnesses and ferroptosis is associated with pathogenic processes of diabetic retinopathy (DR). The goal of this study is to explore the influence of maresin-1 on ferroptosis and its molecular mechanism in DR. Methods ARPE-19 cells were exposed to high glucose (HG) condition for developing a cellular model of DR. The CCK-8 assay and flow cytometry were used to assess ARPE-19 cell proliferation and apoptosis, respectively. Furthermore, the GSH content, MDA content, ROS level, and Fe2+ level were measured by using a colorimetric GSH test kit, a Lipid Peroxidation MDA Assay Kit, a DCFH-DA assay and the phirozine technique, respectively. Immunofluorescence labelling was used to detect protein levels of ACSL4 and PTGS2. Messenger RNA and protein expression of HO-1, GPX4 and Nrf2 was evaluated through western blotting and quantitative real time-polymerase chain reaction (qRT-PCR). To establish a diabetic mouse model, mice were intraperitoneally injected 150 mg/kg streptozotocin. The MDA content, ROS level and the iron level were detected by using corresponding commercial kits. Results Maresin-1 promoted cell proliferation while reducing the apoptotic process in HG-induced ARPE-19 cells. Maresin-1 significantly reduced ferroptosis induced by HG in ARPE-19 cells, as demonstrated as a result of decreased MDA content, ROS level, Fe2+ level, PTGS2 expression, ACSL4 expression and increased GSH content. With respect to mechanisms, maresin-1 treatment up-regulated the mRNA expression and protein expression of HO-1, GPX4 and Nrf2 in HG-induced ARPE-19 cells. Nrf2 inhibitor reversed the inhibitory effects of maresin-1 on ferroptosis in HG-induced ARPE-19 cells. In vivo experiments, we found that Maresin-1 evidently repressed ferroptosis a mouse model of DR, as evidenced by the decreased MDA content, ROS level and iron level in retinal tissues of mice. Conclusion Maresin-1 protects ARPE cells from HG-induced ferroptosis via activating the Nrf2/HO-1/GPX4 pathway, suggesting that maresin-1 prevents DR development.

Over time, the utilization of the Underwater Vehicle-Manipulator System (UVMS) has steadily increased in exploring and harnessing marine resources. However, the underwater environment poses big challenges for controlling, navigating, and communicating with UVMS. These challenges have not only spurred the continuous advancement of related technologies, but also made the development of the UVMS even more captivating. This article firstly provides a review of development status of the UVMS and discusses the current limitations and future directions, and then reviews in detail the dynamic and hydrodynamic modeling methods, and analyzes the principles, advantages, and disadvantages of various approaches. Then, we try to review 2 key technologies of operation control methods, including underwater positioning and navigation technologies and vehicle-manipulator coordinated control approaches. Finally, a reasonable prospect for the future development of UVMS is given.

Abstract Flexible wearable sweat sensors allow continuous, real-time, noninvasive detection of sweat analytes, provide insight into human physiology at the molecular level, and have received significant attention for their promising applications in personalized health monitoring. Electrochemical sensors are the best choice for wearable sweat sensors due to their high performance, low cost, miniaturization, and wide applicability. Recent developments in soft microfluidics, multiplexed biosensing, energy harvesting devices, and materials have advanced the compatibility of wearable electrochemical sweat-sensing platforms. In this review, we summarize the potential of sweat for medical detection and methods for sweat stimulation and collection. This paper provides an overview of the components of wearable sweat sensors and recent developments in materials and power supply technologies and highlights some typical sensing platforms for different types of analytes. Finally, the paper ends with a discussion of the challenges and a view of the prospective development of this exciting field.

Cardiovascular disease (CVD) has become a common health problem of mankind, and the prevalence and mortality of CVD are rising on a year-to-year basis. Blood pressure (BP) is an important physiological parameter of the human body and also an important physiological indicator for the prevention and treatment of CVD. Existing intermittent measurement methods do not fully indicate the real BP status of the human body and cannot get rid of the restraining feeling of a cuff. Accordingly, this study proposed a deep learning network based on the ResNet34 framework for continuous prediction of BP using only the promising PPG signal. The high-quality PPG signals were first passed through a multi-scale feature extraction module after a series of pre-processing to expand the perceptive field and enhance the perception ability on features. Subsequently, useful feature information was then extracted by stacking multiple residual modules with channel attention to increase the accuracy of the model. Lastly, in the training stage, the Huber loss function was adopted to stabilize the iterative process and obtain the optimal solution of the model. On a subset of the MIMIC dataset, the errors of both SBP and DBP predicted by the model met the AAMI standards, while the accuracy of DBP reached Grade A of the BHS standard, and the accuracy of SBP almost reached Grade A of the BHS standard. The proposed method verifies the potential and feasibility of PPG signals combined with deep neural networks in the field of continuous BP monitoring. Furthermore, the method is easy to deploy in portable devices, and it is more consistent with the future trend of wearable blood-pressure-monitoring devices (e.g., smartphones and smartwatches).

Arterial degeneration, closely associated with cardiovascular diseases, is driven by aging-related vascular cell-specific transcriptomics changes. This study provides a single-cell transcriptomic atlas for senile aortic and coronary arteries and underscores FOXO3A-based the transcriptional network in vasoprotection during aging.

Craniopharyngioma is a congenital brain tumor with clinical characteristics of hypothalamic-pituitary dysfunction, increased intracranial pressure, and visual field disorder, among other injuries. Its clinical diagnosis mainly depends on radiological examinations (such as Computed Tomography, Magnetic Resonance Imaging). However, assessing numerous radiological images manually is a challenging task, and the experience of doctors has a great influence on the diagnosis result. The development of artificial intelligence has brought about a great transformation in the clinical diagnosis of craniopharyngioma. This study reviewed the application of artificial intelligence technology in the clinical diagnosis of craniopharyngioma from the aspects of differential classification, prediction of tissue invasion and gene mutation, prognosis prediction, and so on. Based on the reviews, the technical route of intelligent diagnosis based on the traditional machine learning model and deep learning model were further proposed. Additionally, in terms of the limitations and possibilities of the development of artificial intelligence in craniopharyngioma diagnosis, this study discussed the attentions required in future research, including few-shot learning, imbalanced data set, semi-supervised models, and multi-omics fusion.

ObjectivesTo explore the MRI-based differential diagnosis of deep learning with data enhancement for cerebral glioblastoma (GBM), primary central nervous system lymphoma (PCNSL), and tumefactive demyelinating lesion (TDL).Materials and MethodsThis retrospective study analyzed the MRI data of 261 patients with pathologically diagnosed solitary and multiple cerebral GBM (n = 97), PCNSL (n = 92), and TDL (n = 72). The 3D segmentation model was trained to capture the lesion. Different enhancement data were generated by changing the pixel ratio of the lesion and non-lesion areas. The 3D classification network was trained by using the enhancement data. The accuracy, sensitivity, specificity, and area under the curve (AUC) were used to assess the value of different enhancement data on the discrimination performance. These results were then compared with the neuroradiologists’ diagnoses.ResultsThe diagnostic performance fluctuated with the ratio of lesion to non-lesion area changed. The diagnostic performance was best when the ratio was 1.5. The AUCs of GBM, PCNSL, and TDL were 1.00 (95% confidence interval [CI]: 1.000–1.000), 0.96 (95% CI: 0.923–1.000), and 0.954 (95% CI: 0.904–1.000), respectively.ConclusionsDeep learning with data enhancement is useful for the accurate identification of GBM, PCNSL, and TDL, and its diagnostic performance is better than that of the neuroradiologists.

Hepatocellular carcinoma (HCC) is a malignant tumor with high morbidity and mortality on a global scale, and the development of accurate detection and imaging methods for HCC cells is urgently needed. Herein, by connecting peptide L5, which can specifically bind to the overexpressed Glypican-3 (GPC-3) protein of HCC cells with aggregation-induced emission (AIE) moiety ammonium cation-functionalized 9,10-distyrylanthracene (NDSA) via the “click” reaction, we synthesized a fluorescent probe NDSA-L5. In an aqueous solution, the probe shows weak emission, whereas, in the presence of the GPC-3 protein, bright fluorescence can be obtained since NDSA-L5 binds to the GPC-3 protein, leading to the restricted intramolecular movement of AIE-active NDSA-L5. The imaging and flow cytometry experiments demonstrate that the NDSA-L5 probe can rapidly accumulate in the subcutaneous HCC cells and liver tumor tissue and shows a potential application in early detection and surgical navigation for HCC cancer.

MicroRNAs (miRNA) have been identified as oncogenic drivers and tumor suppressors in every major cancer type. In this work, we design an artificial intelligent signal amplification (AISA) system including double-stranded SQ (S, signal strand; Q, quencher strand) and FP (F, fuel strand; P, protect strand) according to thermodynamics principle for sensitive detection of miRNA in vitro and in vivo. In this AISA system for miRNA detection, strand S carries a quenched imaging marker inside the SQ. Target miRNA is constantly replaced by a reaction intermediate and circulatively participates in the reaction, similar to enzyme. Therefore, abundant fluorescent substances from S and SP are dissociated from excessive SQ for in vitro and in vivo visualization. The versatility and feasibility for disease diagnosis using this system were demonstrated by constructing two types of AISA system to detect Hsa-miR-484 and Hsa-miR-100, respectively. The minimum target concentration detected by the system in vitro (10 min after mixing) was 1/10th that of the control group. The precancerous lesions of liver cancer were diagnosed, and the detection accuracy were larger than 94% both in terms of location and concentration. The ability to establish this design framework for AISA system with high specificity provides a new way to monitor tumor progression and to assess therapeutic responses.

The last couple of decades have seen brilliant progress in stem cell therapies, including native, genetically modified, and engineered stem cells, for osteonecrosis of the femoral head (ONFH). In vitro studies evaluate the effect of endogenous or exogenous factor or gene regulation on osteogenic phenotype maintenance and/or differentiation towards osteogenic lineage. The preclinical and clinical outcomes accelerate the clinical translation. Bone marrow mesenchymal stem cells and adipose-derived stem cells have demonstrated better effects in the treatment of femoral head necrosis. Various materials have been used widely in the ONFH treatment in both preclinical and clinical trials. In a word, in vivo and multiscale efforts are expected to overcome obstacles in the approaches for treating ONFH and provide clinical relevance and commercial strategies in the future. Therefore, we will discuss the above aspects in this paper and present our opinions.

Photoacoustic imaging (PAI) has been employed to reconstruct endogenous optical contrast present in tissues. At the cost of longer calculations, a compressive sensing reconstruction scheme can achieve artifact-free imaging with fewer measurements. In this paper, an effective acceleration framework using the alternating direction method (ADM) was proposed for recovering images from limited-view and noisy observations. Results of the simulation demonstrated that the proposed algorithm could perform favorably in comparison to two recently introduced algorithms in computational efficiency and data fidelity. In particular, it ran considerably faster than these two methods. PAI with ADM can improve convergence speed with fewer ultrasonic transducers, enabling a high-performance and cost-effective PAI system for biomedical applications.

The purpose of this study was to noninvasively monitor the therapeutic efficacy of cyclophosphamide (CTX) in a mouse model by dual-modality molecular imaging: positron emission tomography (PET) and bioluminescence imaging (BLI). Firefly luciferase (fLuc) transfected HCC-LM3-fLuc human hepatocellular carcinoma cells were injected subcutaneously into BALB/c nude mice to establish the experimental tumor model. Two groups of HCC-LM3-fLuc tumor-bearing mice ( n = 7 per group) were treated with saline or CTX (100 mg/kg on days 0, 2, 5, and 7). BLI and 18 F-fluorodeoxyglucose ( 18 F-FDG) PET scans were done to evaluate the treatment efficacy. CTX induced a 25.25 ± 13.13% and 35.91 ± 25.85% tumor growth inhibition rate on days 9 and 12 posttreatment, respectively, as determined by BLI. A good linear correlation was found between the tumor sizes measured by caliper and the BLI signals determined by optical imaging ( R 2 = .9216). 18 F-FDG imaging revealed a significant uptake reduction in the tumors of the CTX-treated group compared to that in the saline control group (5.30 ± 1.97 vs 3.00 ± 2.11% ID/g) on day 16 after CTX treatment. Dual-modality molecular imaging using BLI and small-animal PET can play important roles in the process of chemotherapy and will provide noninvasive and reliable monitoring of the therapeutic response.

Molecular imaging, including fluorescence imaging (FMI), bioluminescence imaging (BLI), positron emission tomography (PET), single-photon emission-computed tomography (SPECT), and computed tomography (CT), has a pivotal role in the process of tumor and relevant drug research. CT, especially Micro-CT, can provide the anatomic information for a region of interest (ROI); PET and SPECT can provide functional information for the ROI. BLI and FMI can provide optical information for an ROI. Tumor angiogenesis and relevant drug development is a lengthy, high-risk, and costly process, in which a novel drug needs about 10–15 years of testing to obtain Federal Drug Association (FDA) approval. Molecular imaging can enhance the development process by understanding the tumor mechanisms and drug activity. In this paper, we focus on tumor angiogenesis, and we review the characteristics of molecular imaging modalities and their applications in tumor angiogenesis and relevant drug research.

Since the 1960s, the stem cells have been extensively studied including embryonic stem cells, neural stem cells, bone marrow hematopoietic stem cells, and mesenchymal stem cells. In the recent years, several stem cells have been initially used in the treatment of diseases, such as in bone marrow transplant. At the same time, isolation and culture experimental technologies for stem cell research have been widely developed in recent years. In addition, molecular imaging technologies including optical molecular imaging, positron emission tomography, single-photon emission computed tomography, and computed tomography have been developed rapidly in recent the 10 years and have also been used in the research on disease mechanism and evaluation of treatment of disease related with stem cells. This paper will focus on recent typical isolation, culture, and observation techniques of stem cells followed by a concise introduction. Finally, the current challenges and the future applications of the new technologies in stem cells are given according to the understanding of the authors, and the paper is then concluded. [ABSTRACT FROM AUTHOR]

Bioluminescence tomography (BLT) is a molecular imaging modality which can three-dimensionally resolve the molecular processes in small animals in vivo. The ill-posedness nature of BLT problem makes its reconstruction bears nonunique solution and is sensitive to noise. In this paper, we proposed a sparse BLT reconstruction algorithm based on semigreedy method. To reduce the ill-posedness and computational cost, the optimal permissible source region was automatically chosen by using an iterative search tree. The proposed method obtained fast and stable source reconstruction from the whole body and imposed constraint without using a regularization penalty term. Numerical simulations on a mouse atlas, and in vivo mouse experiments were conducted to validate the effectiveness and potential of the method. [ABSTRACT FROM AUTHOR]