Sensitive Imaging Research Articles

Multifractal Analysis of Choroidal SDOCT Images in the Detection of Retinitis Pigmentosa.

The aim of this paper is to investigate whether a multifractal analysis can be applied to study choroidal blood vessels and help ophthalmologists in the early diagnosis of retinitis pigmentosa (RP). In a case study, we used spectral domain optical coherence tomography (SDOCT), which is a noninvasive and highly sensitive imaging technique of the retina and choroid. The image of a choroidal branching pattern can be regarded as a multifractal. Therefore, we calculated the generalized Renyi point-centered dimensions, which are considered a measure of the inhomogeneity of data, to prove that it increases in patients with RP as compared to those in the control group.

Robust steganographic framework for securing sensitive healthcare data of telemedicine using convolutional neural network

AbstractData is the key element that runs the modern society. Large amounts of data are being released day by day as a result of many activities. The digital data is transferred through the Internet which may be vulnerable to attacks while transmitting. Especially, the medical data is observed to be of at most importance. Thus, the security of the data should be preserved while communicating through the Internet. Many techniques have been evolved for securing the data, such as Encryption which makes the data not understandable by others and techniques such as Steganography, which hides the data inside other file or document. Traditional methods of Steganography‐like LSB substitution are subjected to risks of manipulation of carrier image and poor embedding capabilities. In light of this, the authors introduce a Convolutional Neural Network‐based image steganography method that operates on DICOM images which conceals the sensitive medical data in the carrier file, thereby increasing the privacy of the sensitive data. The data considered here is brain MRI scan images. The Prep Net, Hiding Net and Reveal Net networks, each with distinct number of convolution layers are used to build the model. The sensitive data is firstly segmented to detect the Region of Interest (ROI) and then the combination of sensitive data and the carrier image are fed to the Prep Net to manipulate sensitive images in such a form that it can easily fit inside the carrier image. The Hiding Net takes the result of Prep Net and embeds the sensitive image inside the carrier image to form a stego image. The Reveal Net takes the result of Hiding Net image and then extracts the sensitive image with minimal loss. The dataset contains medical images of DICOM format taken from open source platform Kaggle that contains 3500 images of 2 classes. The results obtained show that the proposed model achieves 20 percent better output compared to traditional LSB methods. As a result, the potential for hiding the data will be expanded, as well as its security.

Role of F-18 FDG PET-CT in neuropsychiatric systemic lupus erythematosus

BackgroundNeuropsychiatric systemic lupus erythematosus (NPSLE) is a major contributor to morbidity and mortality in systemic lupus erythematosus (SLE) patients. To date no single clinical, laboratory or imaging test has proven accurate for NPSLE diagnosis which is a testament to the intricate and multifactorial pathophysiological mechanisms suspected to exist. Functional imaging with FDG PET-CT has shown promise in NPSLE diagnosis, detecting abnormalities prior to changes evident on anatomical imaging. Research indicates that NPSLE may be more aggressive in people of African descent with higher mortality rates, making rapid and correct diagnosis even more important in the African context. MethodsIn this narrative review, we provide a thorough appraisal of the current literature on the role of FDG PET-CT in NPSLE. Large, well-known databases were searched using appropriate search terms. Manual searches of references of retrieved literature were also included. FindingsA total of 73 article abstracts were assessed, yielding 26 papers that were directly relevant to the topic of FDG PET-CT in NPSLE. Results suggest that FDG PET-CT is a sensitive imaging test for NPSLE diagnosis and may play a role in assessing treatment response. It is complementary to routine anatomical imaging, particularly in diffuse manifestations of the disease. Newer quantitative analyses are commonly used for interpretation and can detect even subtle abnormalities, missed on visual inspection. Findings of group-wise analyses of FDG PET-CT scans in NPSLE patients are important in furthering our understanding of the complicated pathophysiological mechanisms involved. Limitations of FDG PET-CT include its lack of specificity, high cost and poor access. ConclusionFDG PET-CT is a sensitive test for NPSLE diagnosis but is hampered by lack of specificity. It is a valuable tool for clinicians managing SLE patients, particularly when anatomical imaging is negative. Its exact application will depend on the local context and clinical scenario.

Design and Optimization of Silver Nanostructured Arrays in Plasmonic Metamaterials for Sensitive Imaging Applications

This paper delves into the strategic design and optimization of silver (Ag) nanostructured arrays within plasmonic metamaterials, targeting the enhancement of imaging sensitivity. Leveraging Finite-Difference Time-Domain (FDTD) simulations, our research rigorously compares various Ag nanostructured geometries, including nanospheres, nanocones, nanodisks, and nanocubes. The aim is to pinpoint configurations that significantly enhance electric field localization on the surfaces of nanostructures, a pivotal factor. The nanocube array exhibits superior field enhancement, particularly in narrow nanogaps, suggesting its suitability for high-sensitivity applications. Further exploration into nanocube arrays reveals the crucial role of nanogap size and spacer layer thickness in tuning the optical properties through the manipulation of Fabry–Pérot and mirror image modes in metal–insulator–metal (MIM) structures. By presenting a thorough analysis of these nanostructured arrays, the study not only contributes to our understanding of the fundamental principles governing plasmonic metamaterials but also provides a solid foundation for future innovation in highly sensitive imaging applications. It underscores the importance of nanostructure design and optimization in achieving significant improvements in the performance of plasmonic devices, marking a pivotal step forward in the field of nanophotonics and its application to sensitive imaging technologies.

Abstract 3529: Leveraging deep learning for fully automated analysis of pre-clinical mouse positron emission tomography

Abstract Turning early-cancer diagnosis into actionable treatment often relies on highly accurate and sensitive imaging techniques to guide surgical intervention or to monitor therapeutic efficacy. Earli is developing a highly sensitive, orthogonal approach that uses genetic constructs to usurp dysregulated cancer pathways to force the tumor to produce a PET reporter gene, enabling the use of radiotracers amenable to positron emission tomography/computed tomography (PET/CT) to guide precision imaging. However, analysis of PET/CT images using standard manual and semi-automated methods is challenging resulting in reduced experimental throughput and user variability, highlighting the need for a fully automated PET/CT analysis pipeline using Deep Learning (DL). To increase pre-clinical throughput and reduce inter-user variability, we developed an automated DL processing pipeline in Python to perform three major tasks: separation of multi-mouse bed data for PET uptake quantification by mouse, segmentation of tumors and background organs, and quantification of PET signal for PK/PD analysis. Preclinical PET/CT data was acquired using a 4 animal, multi-mouse bed and separated into individual mice by co-registering the reconstructed CT image against a multi-mouse bed reference mask using a rigid 3D Euler transformation algorithm and cropping at fixed indices. Following mouse separation, a 3D nnUNet architecture is used to perform semantic segmentation of regions of interest (ROI) on CT for bi-hemispheric subcutaneous tumors, lungs, liver, kidneys, spleen, and the bladder. nnUNet was trained on PET/CT of 311 mice consisting of a mixture of naïve and tumor-bearing animals subcutaneously implanted with H1299 cells using a five-fold cross-validation strategy with 1000 epochs per fold. Model performance was evaluated by comparing the network prediction to the reference manually annotated masks using the Dice coefficient. PET uptake is reported as percent injected dose per milliliter (%ID/mL) or standardized uptake value (SUV). Segmentation performance results for the hold-out set identified mean Dice scores greater than 0.80 for all ROIs, reflecting the similarity between model predictions and human annotations. The automated pipeline reduced the analysis time from 5.5-6 hours per mouse when using traditional manual/semi-automatic approaches to approximately 15 minutes. In summary, we developed a fully automated DL-based PET/CT quantification pipeline for pre-clinical mouse studies that provides accurate ROI segmentation and PET uptake quantification, significantly increasing experimental throughput and reducing inter-user variability. Further improvements to model inference include loss functions weighted for poor performing ROIs and addition of lung nodule segmentation for eventual translation to humans as an accompaniment to Earli’s theranostic programs. Citation Format: Hung-Yu Henry Lee, Mohammed Goryawala, Tim Sproul, Maggie Louie, David Suhy. Leveraging deep learning for fully automated analysis of pre-clinical mouse positron emission tomography [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3529.

A dual-core 3D DNA nanomachine based on DNAzyme positive feedback loop for highly sensitive MicroRNA imaging in living cells

A double 3D DNA walker nanomachine by DNAzyme self-driven positive feedback loop amplification for the detection of miRNA was constructed. This method uses two gold nanoparticles as the reaction core, and because of the spatial confinement effect the local concentration of the reactants increase the collision efficiency was greatly improved. Meanwhile, the introduction of positive feedback loop promotes the conversion efficiency. In presence of miRNA-21, a large amount of DNAzyme was released and hydrolyze the reporter probe, resulting the recovery of fluorescence signal. The linear range for miRNA-21 is 0.5–60 pmol/L, and the detection limit is 0.41 pmol/L (S/N = 3). This nanomachine has been successfully used for accurate detection of miRNA-21 expression levels in cell lysates. At the same time, it can enter cells for intracellular miRNA-21 fluorescence imaging, distinguishing tumor cells from normal cells. This combination of in vitro detection and imaging analysis of living cells can achieve the goal of jointly detecting cancer markers through multiple pathways, providing new ideas for early diagnosis and screening of diseases.

β-Galactosidase-activated near-infrared AIEgen for ovarian cancer imaging in vivo

Near-infrared (NIR) aggregation induced-emission luminogens (AIEgens) circumvent the noisome aggregation-caused quenching (ACQ) effect in physiological milieu, thus holding high promise for real-time and sensitive imaging of biomarkers in vivo. β-Galactosidase (β-Gal) is a biomarker for primary ovarian carcinoma, but current AIEgens for β-Gal sensing display emissions in the visible region and have not been applied in vivo. We herein propose an NIR AIEgen QM-TPA-Gal and applied it for imaging β-Gal activity in vitro and in ovarian tumor model. After being internalized by ovarian cancer cells (e.g., SKOV3), the hydrophilic nonfluorescent QM-TPA-Gal undergoes hydrolyzation by β-Gal to yield hydrophobic QM-TPA-OH, which subsequently aggregates into nanoparticles to turn NIR fluorescence “on” through the AIE mechanism. In vitro experimental results indicate that QM-TPA-Gal has a sensitive and selective response to β-Gal with a limit of detection (LOD) of 0.21 U/mL. Molecular docking simulation confirms that QM-TPA-Gal has a good binding ability with β-Gal to allow efficient hydrolysis. Furthermore, QM-TPA-Gal is successfully applied for β-Gal imaging in SKOV3 cell and SKOV3-bearing living mouse models. It is anticipated that QM-TPA-Gal could be applied for early diagnosis of ovarian cancers or other β-Gal-associated diseases in near future.

Vacuum promoted on-tissue derivatization strategy: Unravelling spatial distribution of glycerides on tissue

Matrix-assisted laser desorption ionization-mass spectrometry imaging (MALDI-MSI) has shown its capability in visualizing the spatial distribution of various kinds of endogenous metabolites. Nevertheless, high quality mass imaging of low polar metabolites remains challenging. Herein, a platform for sensitive matrix-assisted laser desorption ionization-mass spectrometry imaging of cholesterol and glycerides has been proposed. In the platform, a vacuum promoted on-tissue derivatization strategy was proposed to constantly make the derivatization reaction proceed towards to the direction of products. Compared with traditional on-tissue derivatization procedure, the strategy improved the acquired intensity of derivatized glycerides about 50 %. Additionally, the mass spectrometry image reflecting the signal ratio between 3 classes of glycerides was achieved to exploit the metabolic level of glycerides on tissue slice. Finally, the platform was applied to brain slices of Alzheimer's transgenic mice, type 2 diabetes mice and normal mice. Significant difference was found in mass spectrometry images reflecting the signal ratio of multiple endogenous metabolites. The work constructed a promising platform for mapping of glycerides in tissue by mass spectrometry imaging.

Label-Free In Situ Chemical Characterization of Amyloid Plaques in Human Brain Tissues.

The accumulation of amyloid plaques and increased brain redox burdens are neuropathological hallmarks of Alzheimer's disease. Altered metabolism of essential biometals is another feature of Alzheimer's, with amyloid plaques representing sites of disturbed metal homeostasis. Despite these observations, metal-targeting disease treatments have not been therapeutically effective to date. A better understanding of amyloid plaque composition and the role of the metals associated with them is critical. To establish this knowledge, the ability to resolve chemical variations at nanometer length scales relevant to biology is essential. Here, we present a methodology for the label-free, nanoscale chemical characterization of amyloid plaques within human Alzheimer's disease tissue using synchrotron X-ray spectromicroscopy. Our approach exploits a C-H carbon absorption feature, consistent with the presence of lipids, to visualize amyloid plaques selectively against the tissue background, allowing chemical analysis to be performed without the addition of amyloid dyes that alter the native sample chemistry. Using this approach, we show that amyloid plaques contain elevated levels of calcium, carbonates, and iron compared to the surrounding brain tissue. Chemical analysis of iron within plaques revealed the presence of chemically reduced, low-oxidation-state phases, including ferromagnetic metallic iron. The zero-oxidation state of ferromagnetic iron determines its high chemical reactivity and so may contribute to the redox burden in the Alzheimer's brain and thus drive neurodegeneration. Ferromagnetic metallic iron has no established physiological function in the brain and may represent a target for therapies designed to lower redox burdens in Alzheimer's disease. Additionally, ferromagnetic metallic iron has magnetic properties that are distinct from the iron oxide forms predominant in tissue, which might be exploitable for the in vivo detection of amyloid pathologies using magnetically sensitive imaging. We anticipate that this label-free X-ray imaging approach will provide further insights into the chemical composition of amyloid plaques, facilitating better understanding of how plaques influence the course of Alzheimer's disease.

Evaluation of thyroid nodules by high resolution ultrasonography (HRUSG) and its cytological correlation in a teaching hospital

Thyroid nodules are a frequent occurrence. With the widespread use of sensitive imaging methods, a growing number of thyroid nodules are being discovered by chance. High resolution ultrasonography (HRUSG) has emerged as the most appropriate imaging modality for the assessment of thyroid nodules.To Evaluate thyroid nodules by of high resolution ultrasonography (HRUSG) and its correlation with Fine needle aspiration cytology (FNAC). Prospective study done in the department of radiology at Maheshwara medical college and Hospital, Chitkul, near Isnapur, Patancheru, Sangareddy, Telangana for duration of 3 years. A total of 80 cases were studied.HRUSG was done in all cases and Fine Needle Aspiration cytology was done. Radiological and pathological correlation was done and Sensitivity, specificify PPV, NPV and accuracy were calculated. Females were predominant (62.5%) when compared to males (37.5%). Among 80 thyroid nodules , colloid nodule noted in 42.5% cases, Nodular goitre in 27.5%, Hashimotos thyroiditis and follicular adenoma in 12.5%cases and Malignancy in 5% cases HRUSG is a sensitive and specific imaging technique that has a high level of accuracy in the evaluation of thyroid nodules. Solid composition, hypoechogenicity, taller-than-wider form, ill-defined edges, and the presence of calcification are the most effective sonographic markers for predicting cancer.

Evaluation of Features in Probably Benign and Malignant Nonmass Enhancement in Breast MRI.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a highly sensitive breast imaging modality in detecting breast carcinoma. Nonmass enhancement (NME) is uniquely seen on MRI of the breast. The correlation between NME features and pathologic results has not been extensively explored. Our goal was to evaluate the characteristics of probably benign and suspicious NME lesions in MRI and determine which features are more associated with malignancy. We performed a retrospective research after approval by the hospital ethics committee on women who underwent breast MRI from March 2017 to March 2020 and identified 63 lesions of all 400 NME that were categorized as probably benign or suspicious according to the BI-RADS classification (version 2013). MRI features of NME findings including the location, size, distribution and enhancement pattern, kinetic curve, diffusion restriction, and also pathology result or 6-12-month follow-up MRI were evaluated and analyzed in each group (probably benign or suspicious NME). Vacuum-guided biopsies (VAB) were performed under mammographic or sonographic guidance and confirmed with MRI by visualization of the inserted clips. Segmental distribution and clustered ring internal enhancement were significantly associated with malignancy (p value<0.05), while linear distribution or homogeneous enhancement patterns were associated with benignity (p value <0.05). Additionally, the plateau and washout types in the dynamic curve were only seen in malignant lesions (p value <0.05). The presence of DWI restriction in NME lesions was also found to be a statistically important factor. Understanding the imaging findings of malignant NME is helpful to determine when biopsy is indicated. The correlation between NME features and pathologic results is critical in making appropriate management.

Novel Small Molecule Matrix Screening for Simultaneous MALDI Mass Spectrometry Imaging of Multiple Lipids and Phytohormones.

Most of the traditional matrices cannot simultaneously image multiple lipids and phytohormones, so screening and discovery of novel matrices stand as essential approaches for broadening the application scope of matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). In this work, 12 organic small molecule compounds were comprehensively screened and investigated as potential MALDI matrices for simultaneous imaging analysis of various lipids and phytohormones. In the positive ionization mode, p-nitroaniline, m-nitroaniline, and 2-aminoterephthalic acid displayed good performance for the highly sensitive detection of lysophosphatidylcholines (LPCs), phosphatidylcholines (PCs), and triacylglycerols (TGs). Furthermore, p-nitroaniline possessed excellent characteristics of strong ultraviolet absorption and homogeneous cocrystallization, making it a desirable matrix for MALDI-MSI analysis of eight plant hormones. Compared with conventional matrices (2,5-dihydroxybenzoic acid (DHB), α-cyano-4-hydroxycinnamic acid (CHCA), and 9-aminoacridine (9-AA), the use of p-nitroaniline resulted in higher ionization efficiency, superior sensitivity, and clearer imaging images in dual polarity mode. Our research offers valuable guidance and new ideas for future endeavors in matrix screening.

A chaotic based image encryption scheme using elliptic curve cryptography and genetic algorithm

In the era of digital communication and data security, image encryption plays a crucial role in safeguarding sensitive information. Protecting sensitive visual data from unauthorized access drives the pursuit of advanced image encryption methods. This paper proposes a novel approach to enhance image encryption by combining the power of a chaotic map, elliptic curve cryptography, and genetic algorithm. The chaotic map, specifically Arnold’s cat map, is employed to introduce chaos and randomness into the encryption process. The proposed image encryption process involves applying Arnold’s cat map for shuffling the pixel positions, followed by elliptic curve cryptography for encrypting the pixel values using public and private keys. Additionally, a genetic algorithm is employed to optimize the key generation process, enhancing the security of the encryption scheme. The combined utilization of these techniques aims to achieve a high level of confidentiality and robustness in image encryption. The algorithm underwent thorough analysis. It achieved a maximum entropy score of 7.99, indicating a high level of randomness and unpredictability in the encrypted data. Additionally, it exhibited near-zero correlation, which suggests strong resistance against statistical attacks. Moreover, the cryptographic range of possible keys was found to be 2511\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$2^{511}$$\\end{document}. This extensive key space makes the algorithm highly resilient against brute force attacks. It took only 0.5634 s to encrypt a moderately sized 512×512\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$512\ imes 512$$\\end{document} pixel image with an 8-bit image on a standard desktop computer with a 2.3 GHz processor and 16 GB of RAM. The experimental findings confirm that the proposed approach is highly effective and efficient in safeguarding sensitive image data from unauthorized access and potential attacks. This scheme has the benefit of allowing us to protect our private image data while it’s being transmitted.

Correlation between Spring Wheat Physiological Indicators and UAV Digital Image Index in Hetao Irrigation Area

To accurately and non-destructively monitor the growth of spring wheat in the Hetao irrigation area, UAV remote sensing was employed during various fertility stages. Digital image indices from diverse fertilization treatments were calculated and compared with physiological indices to identify the most sensitive digital image indices corresponding to these indices. The study underscored the critical importance of the flowering stage in the growth of spring wheat, thus highlighting the necessity of focusing on this stage. This finding reiterated that the flowering stage was pivotal for spring wheat development in the Hetao Irrigation Area. Several digital image indices, such as GLA, R, G, INT, g, GRVI, MGRVI, RGBVI, EXG, and GRRI, exhibited a high frequency of significant correlations with physiological indices during the four primary reproductive stages of wheat. Consequently, these sensitive digital image indices during the flowering stage can more effectively characterize the physiological indices of spring wheat.

Sensitive bioluminescence imaging of cryptococcosis in Galleria mellonella improves antifungal screening under in vivo conditions

ABSTRACT Cryptococcus neoformans is an environmental yeast that primarily affects immunocompromised individuals, causing respiratory infections and life-threatening meningoencephalitis. Treatment is complicated by limited antifungal options, with concerns such as adverse effects, dose-limiting toxicity, blood–brain barrier permeability, and resistance development, emphasizing the critical need to optimize and expand current treatment options against invasive cryptococcosis. Galleria mellonella larvae have been introduced as an ethical intermediate for in vivo testing, bridging the gap between in vitro antifungal screening and mouse studies. However, current infection readouts in G. mellonella are indirect, insensitive, or invasive, which hampers the full potential of the model. To address the absence of a reliable non-invasive method for tracking infection, we longitudinally quantified the cryptococcal burden in G. mellonella using bioluminescence imaging (BLI). After infection with firefly luciferase-expressing C. neoformans, the resulting bioluminescence signal was quantitatively validated using colony-forming unit analysis. Longitudinal comparison of BLI to health and survival analysis revealed increased sensitivity of BLI in discriminating cryptococcal burden during early infection. Furthermore, BLI improved the detection of treatment efficacy using first-line antifungals, thereby benchmarking this model for antifungal testing. In conclusion, we introduced BLI as a real-time, quantitative readout of cryptococcal burden in G. mellonella over time, enabling more sensitive and reliable antifungal screening.

Self-Feedback DNAzyme Motor for Cascade-Amplified Imaging of mRNA in Live Cells and In Vivo.

DNA motors have attracted extensive interest in biosensing and bioimaging. However, the amplification capacity of the existing DNA motor systems is limited since the products from the walking process are unable to feedback into the original DNA motor systems. As a result, the sensitivities of such systems are limited in the contexts of biosensing and bioimaging. In this study, we report a novel self-feedback DNAzyme motor for the sensitive imaging of tumor-related mRNA in live cells and in vivo with cascade signal amplification capacity. Gold nanoparticles (AuNPs) are modified with hairpin-locked DNAzyme walker and track strands formed by hybridizing Cy5-labeled DNA trigger-incorporated substrate strands with assistant strands. Hybridization of the target mRNA with the hairpin strands activates DNAzyme and promotes the autonomous walking of DNAzyme on AuNPs through DNAzyme-catalyzed substrate cleavage, resulting in the release of many Cy5-labeled substrate segments containing DNA triggers and the generation of an amplified fluorescence signal. Moreover, each released DNA trigger can also bind with the hairpin strand to activate and operate the original motor system, which induces further signal amplification via a feedback mechanism. This motor exhibits a 102-fold improvement in detection sensitivity over conventional DNAzyme motors and high selectivity for target mRNA. It has been successfully applied to distinguish cancer cells from normal cells and diagnose tumors in vivo based on mRNA imaging. The proposed DNAzyme motor provides a promising paradigm for the amplified detection and sensitive imaging of low-abundance biomolecules in vivo.

Live Cell Imaging and Real-Time Monitoring of Nucleolus Morphology and Mitophagy with a Red Fluorescent and Photostable rRNA-Specific Probe in Human Cancer Cells.

rRNAs are prevalent in living organisms. They are produced in nucleolus and mitochondria and play essential cellular functions. In addition to the primary biofunction in protein synthesis, rRNAs have been recognized as the emerging signaling molecule and drug target for studies on nucleolus morphology, mitochondrial autophagy, and tumor cell malignancy. Currently, only a few rRNA-selective probes have been developed, and most of them encounter the drawbacks of low water solubility, poor nuclear membrane permeability, short emission wavelength, low stability against photobleaching, and high cytotoxicity. These unfavorable properties of rRNA probes limit their potential applications. In the present study, we reported a new rRNA-selective and near-infrared fluorescent turn-on probe, 4MPS-TO, capable of tracking rRNA in live human cancer cells. The real-time monitoring performance in nucleolus morphology and mitochondrial autophagy is demonstrated in HeLa cells. The probe shows great application potential for being used as a rRNA-selective, sensitive, and photostable imaging tool in chemical biology study and drug screening.

Post-Discectomy Infection: A Critical Review and Suggestion of a Management Algorithm.

Postoperative discitis (POD) accounts for 20% to 30% of all cases of pyogenic spondylodiscitis, while POD may be mis-or-under-diagnosed, due to the vague related symptomatology and the non-specific imaging findings. Most studies report infection rate of less than 1%, which increases with the addition of non-instrumented fusion to 2.4% to 6.2%. It remains controversial whether POD is caused by an aseptic or infectious process. Positive cultures are presented only in 42-73% of patients with Staphylococcus species being the most common invading organisms, while Staphylococcus aureus is isolated in almost 50% of cases. The onset of POD symptoms usually occurs at 2-4 weeks after an apparently uneventful operation. Back pain and muscle spasms are usually refractory to bed rest and analgesics. Magnetic Resonance Imaging (MRI) is the most sensitive and specific imaging diagnostic technique. Antimicrobial therapy depends on the results of tissue cultures, and along with bracing represents the mainstay of management. Surgical intervention is necessary in patients failing conservative treatment. For the majority of cases, extensive surgical debridement, antibiotic therapy, and orthosis immobilization are effective in eliminating the infection. According to this, we recommend an Algorithmic approach for the management of POD. Postoperative infections after spinal surgery pose a certain clinical challenge, and in most cases can be treated conservatively. Nevertheless, disability may be persistent, and surgery could be necessary. The purpose of this concise review is to describe the manifestation of post-discectomy infection, its pathogenesis and particularly a rational approach for its management.

Clinical features, laboratory findings and treatment of juxtaglomerular cell tumors: a systemic review.

Juxtaglomerular cell tumors (JGCTs) or reninoma are rare kidney tumors leading to secondary hypertension, and the non-specific clinical manifestations bring about challenges to the diagnosis. This study is to summarize the clinical features, laboratory findings, and treatment of JGCTs. The PubMed, EMBASE database, and manual search were utilized to find all cases, and 158 reports containing 261 patients were identified. Data on patients' demographics, clinical features, diagnostic methods, and treatment options were collected and analyzed. JGCTs occurred predominantly in female patients (female to male ratio, 2.1:1). The median age of patients was 25 years (IQR:18-34 years). Hypertension (97.24%) was the cardinal manifestation. Hypokalemia was reported in 78.71% (159/202) of subjects, and normal serum potassium accounted for 20.79% (42/202). In cases with assessed plasma renin activity (PRA) levels, the median PRA was 7.89 times the upper limit of normal (IQR:3.58-14.41), and 3.82% (5/131) of cases in the normal range. Tumors were detected in 97.8% (175/179) computed tomography (CT), 94.7% (72/76) magnetic resonance imaging (MRI), and 81.5% (110/135) ultrasound, respectively. For 250/261 patients undergoing surgical procedures, 89.14% (197/221), 94.94% (150/158), and 100% (131/131) of patients were restored to normal blood pressure, PRA, and serum potassium, respectively. JGCTs are commonly associated with hypertension, hypokalemia, and hyperreninemia, whereas patients with normotension, normokalemia, and PRA should be systematically pursued after drug-elution lasting for 2 weeks. CT and MRI are more sensitive imaging diagnostic methods. The blood pressure and biochemical parameters of most patients returned to normal after surgery.

THz near-field intensity distribution imaging in the 0.3 THz band using a highly sensitive polarization CMOS image sensor using a 0.35 μm CMOS process

In this paper, we propose a low-disturbance and fast terahertz (THz) near-field intensity distribution imaging method. The THz detector is fabricated using an oriented multiwalled carbon nanotube (CNT) thin film and a LiNbO3(LN) crystal to the thin film is attached. The CNT absorbs and converts THz waves into heat, and the birefringence change of the LN crystal owing to the heat is used. The birefringence change was measured with high sensitivity using a dual-polarizer configuration of a uniform polarizer and a polarization CMOS image sensor. The fabricated THz detector is a low-disturbance method because it does not use metal, and it can measure the THz distribution in the plane all at once, which is faster than the antenna scanning method. Using the proposed method, we have successfully imaged the THz near-field intensity distribution emitted from an impact avalanche and transit time diode oscillating at 0.278 THz.