Structural Similarity Research Articles

Two novel plasmids harboring the multiresistance gene cfr in porcine Staphylococcus equorum

BackgroundThe emergence and transmission of the multidrug resistance gene cfr have raised public health concerns worldwide. ObjectivesMultidrug-resistant Staphylococcus equorum isolates can pose a threat to public health. In this study, we have characterised the whole-genome of one Staphylococcus equorum isolate harboring two distinct cfr-carrying plasmids. MethodsAntimicrobial susceptibility testing was performed by broth microdilution. Genomic DNA was sequenced using both the Illumina HiSeq X Ten and Nanopore MinION platforms. De novo hybrid assembly was performed by Unicycler. Genomic data were assessed by in silico prediction and bioinformatic tools. ResultsStaphylococcus equorum isolate SN42 exhibited resistance or high MICs to linezolid, erythromycin, tetracycline, oxacillin, clindamycin, virginiamycin, tiamulin, chloramphenicol and florfenicol. It carried two cfr-harboring plasmids: the RepA N-family plasmid pSN42-51K and the Inc18-family plasmid pSN42-50K. These two plasmids exhibited low structural similarities to the so far reported cfr-carrying plasmids. Both plasmids harbored an arsenic resistance operon, copper and cadmium resistance genes as well as the lincosamide-pleuromutilin-streptogramin A resistance gene lsa(B). In addition, plasmid pSN42-51K carried two erm(B) genes for macrolide-lincosamide-streptogramin B resistance, the streptomycin resistance gene ant(6)-Ia as well as mercury resistance genes while pSN42-50K was associated with the heavy metal translocating P-type ATPase gene hmtp. The co-carriage and co-existence of these antimicrobial resistance and heavy metal resistance genes increases the likelihood of co-selection of the cfr-carrying plasmids. ConclusionThis is the first report of S. equorum carrying two distinct cfr-carrying plasmids, underscoring the need for ongoing surveillance to address the potential dissemination of multi-drug resistance in bacteria from food-producing animals to ensure food safety and public health.

Recent trends in neuromorphic systems for non-von Neumann in materia computing and cognitive functionalities

In the era of artificial intelligence and smart automated systems, the quest for efficient data processing has driven exploration into neuromorphic systems, aiming to replicate brain functionality and complex cognitive actions. This review assesses, based on recent literature, the challenges and progress in developing basic neuromorphic systems, focusing on “material-neuron” concepts, that integrate structural similarities, analog memory, retention, and Hebbian learning of the brain, contrasting with conventional von Neumann architecture and spiking circuits. We categorize these devices into filamentary and non-filamentary types, highlighting their ability to mimic synaptic plasticity through external stimuli manipulation. Additionally, we emphasize the importance of heterogeneous neural content to support conductance linearity, plasticity, and volatility, enabling effective processing and storage of various types of information. Our comprehensive approach categorizes fundamentally different devices under a generalized pattern dictated by the driving parameters, namely, the pulse number, amplitude, duration, interval, as well as the current compliance employed to contain the conducting pathways. We also discuss the importance of hybridization protocols in fabricating neuromorphic systems making use of existing complementary metal oxide semiconductor technologies being practiced in the silicon foundries, which perhaps ensures a smooth translation and user interfacing of these new generation devices. The review concludes by outlining insights into developing cognitive systems, current challenges, and future directions in realizing deployable neuromorphic systems in the field of artificial intelligence.

Network pharmacology analysis revealed the mechanism and active compounds of Jiao tai wan in the treatment of type 2 diabetes mellitus via SRC/PI3K/AKT signaling

Ethnopharmacological relevanceJiao-tai-wan (JTW) is a traditional Chinese herbal prescription, exerts its therapeutic effects on type 2 diabetes mellitus (T2DM). However, its mechanisms and active components remain unclear. Aim of the studyTo investigate the therapeutic mechanisms of JTW in treating type 2 diabetes mellitus (T2DM), focusing on identifying active components, their targets, and validating efficacy through SRC/PI3K/AKT signaling pathway modulation in vitro and in vivo. Materials and methodsActive ingredients were retrieved from the Traditional Chinese Medicine System Pharmacology (TCMSP) and Comprehensive Traditional Chinese Medicine Database (TCMID). Targets for these components were identified using the ChemMapper database based on 3D structural similarity. T2DM-related genes were sourced from the DisGeNET and Gene Expression Omnibus (GEO) databases. Protein-protein interaction (PPI) analysis and functional enrichment analysis were conducted to construct a pathway network of “herbs-active ingredients-candidate targets”, identifying core molecular mechanisms and key active ingredients. SwissDock was used for molecular docking to predict ligands for candidate targets. The diabetic models were established using C57BL/6 mice and human liver HepG2 cell lines. Their Effectiveness and key molecules were verified through biochemical detection and immunoblotting. ResultsTotal 30 active compounds, 597 active ingredient targets, 9631 T2DM-related genes, and 521 overlapping candidate targets were found for JTW on T2DM. Go enrichment indicated the core pathways enriched on insulin and glucose metabolism. The auto-docking demonstrated SRC has potential binds to ingredients of JTW. In vivo, JTW can reduce blood glucose, and blood lipid levels, and HOMA-IR, and increase HOMA-ISI levels in T2DM mice with reduced ALT, AST, MDA levels and increased SOD levels. Meanwhile, decreased phosphorylation of SRC, along with increased levels of phosphorylated PI3K, PI3K, and phosphorylated AKT, were observed. HE staining of liver tissues further confirmed that JTW administration improved liver morphology, reducing inflammation and necrosis. In vitro, JTW significantly ameliorates upstream dysregulation by reducing SRC phosphorylation while enhancing phosphorylated PI3K, PI3K, and AKT phosphorylation levels. ConclusionJTW may alleviate glucose, insulin resistance, and lipid metabolism disorders by the SRC/PI3K/AKT signaling pathway, that provide a novel view of potential active compounds and essential targets in treating T2DM.

Static X-ray differential phase contrast imaging via bidirectional-misalignment grating

Grating-based X-ray differential phase contrast imaging (GB-DPCI) offers excellent imaging contrast for soft tissues. Limited by the phase stepping of gratings, the conventional GB-DPCI leads to a high radiation dose. This study presents a static GB-DPCI method employing an analyzer grating with a misaligned structure in both vertical and horizontal directions. A signal retrieval algorithm is developed for this method to permit the use of neighboring pixels on the detector to approximate multiple phase stepping positions, thereby obtaining the phase contrast signal in a single exposure. This study reduces high radiation doses and time-consumption caused by phase stepping. The presented method is validated with datasets from a laboratory X-ray tube and synchrotron radiation. Considering synchrotron results as example, minimal improvements in Feature Similarity Index Measure are 2.48%, 6.31%, and 7.06% for absorption, dark field, and phase contrasts, and in Information-Weight Structure Similarity Index Measure are 3.24%, 16.07%, and 8.61%.

A Hybrid System Based on Three Levels to Hide Information using JPEG Color Images

Nowadays, due to the circulation of information among people, information security has become an important requirement to protect information from hacking. Many techniques have been discovered to protect this information and maintain its confidentiality from hacking, the most prominent of which are cryptography and steganography techniques. In this paper, a hybrid system based on three levels to hide information using Joint Photographic Experts Group (JPEG) color images, where this system works in both processes sender and receiver. The sender process at the first level uses a mixing of the Advanced Encryption Standard (AES) and Rivest Shamir Adleman (RSA), while at the second level uses a Fuzzy Stream Algorithm (FSA) adds a higher complexity and eliminates the non-linearity of the encrypted information. The third level is to hide encrypted information using the Least Significant Bit (LSB) technique, while the receiver process, performs the reverse process of three levels. This system provides high information embedding capability and the inability to perceive hidden information, the system was evaluated based on criteria like Peak Signal Noise Ratio (PSNR), Mean Square Error (MSE), Structure Similarity (SSIM), and correlation, which were characterized by high and good rates. The study was compared with modern steganography techniques.

Combined LC-MS/MS feature grouping, statistical prioritization, and interactive networking in msFeaST.

Computational metabolomics workflows have revolutionized the untargeted metabolomics field. However, the organization and prioritization of metabolite features remains a laborious process. Organizing metabolomics data is often done through mass fragmentation-based spectral similarity grouping, resulting in feature sets that also represent an intuitive and scientifically meaningful first stage of analysis in untargeted metabolomics. Exploiting such feature sets, feature-set testing has emerged as an approach that is widely used in genomics and targeted metabolomics pathway enrichment analyses. It allows for formally combining groupings with statistical testing into more meaningful pathway enrichment conclusions. Here, we present msFeaST (mass spectral Feature Set Testing), a feature-set testing and visualization workflow for LC-MS/MS untargeted metabolomics data. Feature-set testing involves statistically assessing differential abundance patterns for groups of features across experimental conditions. We developed msFeaST to make use of spectral similarity-based feature groupings generated using k-medoids clustering, where the resulting clusters serve as a proxy for grouping structurally similar features with potential biosynthesis pathway relationships. Spectral clustering done in this way allows for feature group-wise statistical testing using the globaltest package, which provides high power to detect small concordant effects via joint modeling and reduced multiplicity adjustment penalties. Hence, msFeaST provides interactive integration of the semi-quantitative experimental information with mass-spectral structural similarity information, enhancing the prioritization of features and feature sets during exploratory data analysis. The msFeaST workflow is freely available through https://github.com/kevinmildau/msFeaST and built to work on MacOS and Linux systems. Supplementary information is available at Bioinformatics online.

Thin Cloud Removal Generative Adversarial Network Based on Sparse Transformer in Remote Sensing Images

Thin clouds in Remote Sensing (RS) imagery can negatively impact subsequent applications. Current Deep Learning (DL) approaches often prioritize information recovery in cloud-covered areas but may not adequately preserve information in cloud-free regions, leading to color distortion, detail loss, and visual artifacts. This study proposes a Sparse Transformer-based Generative Adversarial Network (SpT-GAN) to solve these problems. First, a global enhancement feature extraction module is added to the generator’s top layer to enhance the model’s ability to preserve ground feature information in cloud-free areas. Then, the processed feature map is reconstructed using the sparse transformer-based encoder and decoder with an adaptive threshold filtering mechanism to ensure sparsity. This mechanism enables that the model preserves robust long-range modeling capabilities while disregarding irrelevant details. In addition, inverted residual Fourier transformation blocks are added at each level of the structure to filter redundant information and enhance the quality of the generated cloud-free images. Finally, a composite loss function is created to minimize error in the generated images, resulting in improved resolution and color fidelity. SpT-GAN achieves outstanding results in removing clouds both quantitatively and visually, with Structural Similarity Index (SSIM) values of 98.06% and 92.19% and Peak Signal-to-Noise Ratio (PSNR) values of 36.19 dB and 30.53 dB on the RICE1 and T-Cloud datasets, respectively. On the T-Cloud dataset, especially with more complex cloud components, the superior ability of SpT-GAN to restore ground details is more evident.

Characterization and functional analysis of Toxoplasma Golgi-associated proteins identified by proximity labeling.

Apicomplexan parasites such as Toxoplasma gondii infect a large percentage of the world's population and cause substantial human disease. These widespread pathogens use specialized secretory organelles to infect their host cells, modulate host cell functions, and cause disease. While the functions of the secretory organelles are now better understood, the Golgi apparatus of the parasite remains largely unexplored, particularly regarding parasite-specific innovations that may help direct traffic intracellularly. In this work, we characterize ULP1, a protein that is unique to parasites but shares structural similarity to the eukaryotic trafficking factor p115/Uso1. We show that ULP1 plays an important role in parasite fitness and demonstrate that it interacts with the conserved oligomeric Golgi (COG) complex. We then use ULP1 proximity labeling to identify 11 additional Golgi-associated proteins, which we functionally analyze via conditional knockdown. This work expands our knowledge of the Toxoplasma Golgi apparatus and identifies potential targets for therapeutic intervention.

Molecular structure similarity analysis using Tanimoto coefficient and its correlation analysis with Maltase-glucoamylase inhibitory activity of Nigella sativa’s compounds

Nigella sativa is one of the medicinal plants that are efficacious for treating diabetes mellitus. Diabetes mellitus is a chronic metabolic disorder characterized by hyperglycemia due to damage of insulin action, insulin production, and/or both. In this study, the molecular structure similarity analysis of the compounds in Nigella sativa to acarbose and the correlation analysis of the similarity with its activity as antidiabetic with the mechanism of maltase glucoamylase (MGAM) inhibition was carried out. Similarity analysis has been done used Tanimoto coefficient. The prediction of MGAM inhibitory activity has been done using molecular docking with molegro virtual docker. The Kaempferol 3-glucosyl-(1-2)-galactosyl-(1-2)-glucoside; (S)-2,3-Epoxysqualene; Quercetin 3-glucosyl-(1-2)-galactosyl-(1-2)-glucoside; Oleic Acid has activity as an inhibitor of MGAM with rerank score -107.8770, -102.1760, -95.7338, -92.4246 respectively and these has Tanimoto score 0.426, 0.319, 0.413, 0.357 respectively. The correlation analysis obtained that there is a significant relationship between the Tanimoto Coefficient and Rerank Score with the opposite relationship because the correlation value is negative. Greater the degree of molecular structure similarity of Nigella sativa’s compounds to acarbose more likely has the similar biological activity as MGAM inhibitory

Bibliometric Analysis of Pyrimidine Compounds with Anti-cancer Activity: Research Trends from 2015 to 2023

Cancer remains one of the most pervasive diseases in the world, globally, leading to millions of fatalities annually. Currently, numerous anti-cancer treatments are available to address the various types of cancer. Pyrimidine, a class of heterocyclic nitrogenous compounds, holds substantial promise in anti-cancer drug development due to its structural similarity to the nucleotide base pairs found in DNA and RNA. This work aimed to conduct a bibliometric analysis of studies on pyrimidine compounds with anti-cancer characteristics. Using the Scopus database, we examined literature published between 2015 and 2023, explicitly concentrating on pyrimidine derivatives as potential anti-cancer agents. Data analysis such as network analysis, co-occurrence, and visualization were conducted utilizing VOSviewer 1.6.13. Nine hundred twenty-two papers altogether, including reviews and original research, discussing the anti-cancer activity of pyrimidine compounds were identified within the specified timeframe up to January 10, 2023. Among these, 922 journal articles were selected for further analysis. Results show that Egypt led in research productivity with 267 articles (28.96%), followed by India with 234 articles (25.38%), and China with 163 articles (17.68%). Cairo University in Egypt ranked first in institutional productivity with 9 articles (0.98%), while the journal Bioorganic Chemistry was the most prolific, publishing 62 articles (6.72%) on pyrimidine compounds with anti-cancer activity. The notable increase in publications on the anti-cancer activity of pyrimidine compounds in recent years underscores the significant interest they have garnered from both pharmaceutical and academic researchers. The data presented in this study will serve as a foundational reference for future comparative studies in this area.

Development and Validation of LC-MS method for determination of Genotoxic Impurity N, O-Dimethyl Hydroxylamine by Chemical Derivatization in Investigational Drug Substance

Background: Conventional chromatographic techniques and direct analysis pose challenges in detecting certain potential genotoxic impurities, primarily due to factors such as the absence of a chromophore, low molecular weight, and insufficient chromatographic retention caused by high polarity. One such compound, N, O-Dimethyl Hydroxylamine, is of particular concern due to its structural similarity to Hydroxylamine, a recognized genotoxic impurity. Method: In response to this challenge, this article proposes a rapid derivatization approach utilizing Dansyl Chloride, coupled with Liquid Chromatography-Mass Spectrometry (LC-MS) for the detection of the derivatized product in the investigational drug substance. To preserve confidentiality, the specific name and structure of the drug substance have not been disclosed. Results: The developed method exhibits a reliable range of detection spanning from 5 ppm to 60 ppm with respect to a nominal drug substance concentration of 10 mg/mL. Throughout the method validation process, it demonstrated specificity, sensitivity, linearity, accuracy, and repeatability. Conclusion: This methodology presents a versatile solution applicable in scenarios where N, ODimethyl Hydroxylamine serves as a reagent in the synthesis process of drug substances.

IMCSN: An improved neighborhood aggregation interaction strategy for multi-scale contrastive Siamese networks

Graph contrastive learning is an effective method for enhancing graph representation by maximizing the similarity of representations between analogous graphs while minimizing the similarity between dissimilar ones. A common approach to improve the representation capabilities of graphs involves data augmentation, which generates additional training samples through transformations applied to the original graphs. However, traditional data augmentation techniques, such as random deletion of nodes or edges, often compromise the structural integrity of graphs, result in loss of crucial information, and lead to representation instability. To overcome these drawbacks, this study introduces a novel data augmentation paradigm, the integration of directed random noise (IDR), which incorporates controlled random noise to optimize the augmentation objectives. IDR enhances the diversity and robustness of representations without sacrificing the structural integrity of the graphs. This approach significantly improves the performance of graph contrastive learning, avoiding the common issues of graph structure damage and information loss associated with conventional methods. To further refine the model, this paper proposes an improved multiscale contrastive Siamese network framework that employs three Siamese networks to process different views of input graphs. It utilizes cross-network and cross-view contrastive learning objectives to optimize graph representations, leveraging the complementary information between different views to maximize the consistency of representations among similar graphs. This enhancement improves the quality and generalizability of graph representations. Additionally, the introduction of a self-supervised loss function based on graph reconstruction within the loss framework capitalizes on the structural similarity between the original and reconstructed graphs to further refine graph representations. This loss function ensures that the global view retains more structural information from the original graph, thus enhancing the complementarity between the global and local views. The effectiveness and stability of this research framework are demonstrated through node classification tasks and visualizations on six real-world datasets, comparing favorably against existing methods such as MERIT and GraphVAT. The proposed model achieves accuracy improvements of 3.21 % and 3.71 % on the Cora dataset and 1.12 % and 1.42 % on the CiteSeer dataset. Additionally, it ranks first in an average accuracy comparison across six datasets, with scores of 80.6 % and 52.67 %. These results underscore the robustness and efficacy of the proposed model in self-supervised graph representation learning, offering substantial advancements over existing techniques.

MGA-Net: A novel mask-guided attention neural network for precision neonatal brain imaging

In this study, we introduce MGA-Net, a novel mask-guided attention neural network, which extends the U-net model for precision neonatal brain imaging. MGA-Net is designed to extract the brain from other structures and reconstruct high-quality brain images. The network employs a common encoder and two decoders: one for brain mask extraction and the other for brain region reconstruction. A key feature of MGA-Net is its high-level mask-guided attention module, which leverages features from the brain mask decoder to enhance image reconstruction. To enable the same encoder and decoder to process both MRI and ultrasound (US) images, MGA-Net integrates sinusoidal positional encoding. This encoding assigns distinct positional values to MRI and US images, allowing the model to effectively learn from both modalities. Consequently, features learned from a single modality can aid in learning a modality with less available data, such as US. We extensively validated the proposed MGA-Net on diverse and independent datasets from varied clinical settings and neonatal age groups. The metrics used for assessment included the DICE similarity coefficient, recall, and accuracy for image segmentation; structural similarity for image reconstruction; and root mean squared error for total brain volume estimation from 3D ultrasound images. Our results demonstrate that MGA-Net significantly outperforms traditional methods, offering superior performance in brain extraction and segmentation while achieving high precision in image reconstruction and volumetric analysis. Thus, MGA-Net represents a robust and effective preprocessing tool for MRI and 3D ultrasound images, marking a significant advance in neuroimaging that enhances both research and clinical diagnostics in the neonatal period and beyond.

Comparative Approach to De-Noising TEMPEST Video Frames

Analysis of unintended compromising emissions from Video Display Units (VDUs) is an important topic in research communities. This paper examines the feasibility of recovering the information displayed on the monitor from reconstructed video frames. The study holds particular significance for our understanding of security vulnerabilities associated with the electromagnetic radiation of digital displays. Considering the amount of noise that reconstructed TEMPEST video frames have, the work in this paper focuses on two different approaches to de-noising images for efficient optical character recognition. First, an Adaptive Wiener Filter (AWF) with adaptive window size implemented in the spatial domain was tested, and then a Convolutional Neural Network (CNN) with an encoder–decoder structure that follows both classical auto-encoder model architecture and U-Net architecture (auto-encoder with skip connections). These two techniques resulted in an improvement of more than two times on the Structural Similarity Index Metric (SSIM) for AWF and up to four times for the SSIM for the Deep Learning (DL) approach. In addition, to validate the results, the possibility of text recovery from processed noisy frames was studied using a state-of-the-art Tesseract Optical Character Recognition (OCR) engine. The present work aims to bring to attention the security importance of this topic and the non-negligible character of VDU information leakages.

Triagem in silico de compostos com atividade ansiolítica encontrados na espécie Magnolia obovata

Magnolia obovata, known as “Japanese cucumber”, is a deciduous tree of Asian origin, constituting a medicinal plant due to its anti-inflammatory, anxiolytic, antidepressant effects, among other central effects, already demonstrated in the literature. The objective of this study was to suggest the mechanisms of action for the effects on the central nervous system of the compounds identified in the species M. obovata, especially regarding the anxiolytic effect currently sought with the use of the plant. Nineteen compounds present in M. obovata were identified, with only 2 molecules (alpha-eudesmol and gamma-eudesmol) showing in silico pharmacokinetic and toxicological properties favorable to anxiolytic bioactivity. Such molecules inhibit acylcarnitine hydrolase and increase free acylcarnitine, possibly generating an anxiolytic effect. Pharmacophoric modeling of those molecules showed 6 interaction points with the 5 most potent known ligands of acylcarnitine hydrolase and such structural similarity is promising for acting on this target. There are advantages of the alternative mechanism of action of this compound in relation to current anxiolytics, which could be used to formulate new therapies in the treatment of anxiety disorders. The results obtained here open perspectives for tests in in vitro and in vivo models, aiming to confirm the results of the computational analyses.

Structures of the Varicella Zoster Virus Glycoprotein E and Epitope Mapping of Vaccine-Elicited Antibodies

Background: Varicella zoster virus (VZV) is the causative agent for chickenpox and herpes zoster (HZ, shingles). HZ is a debilitating disease affecting elderly and immunocompromised populations. Glycoprotein E (gE) is indispensable for viral replication and cell-to-cell spread and is the primary target for anti-VZV antibodies. Importantly, gE is the sole antigen in Shingrix, a highly efficacious, AS01B-adjuvanted vaccine approved in multiple countries for the prevention of HZ, yet the three-dimensional (3D) structure of gE remains elusive. Objectives: We sought to determine the structure of VZV gE and to understand in detail its interactions with neutralizing antibodies. Methods: We used X-ray crystallography and cryo-electron microscopy to elucidate structures of gE bound by recombinant Fabs of antibodies previously elicited through vaccination with Zostavax, a live, attenuated vaccine. Results: The 3D structures resolve distinct central and C-terminal antigenic domains, presenting an array of diverse conformational epitopes. The central domain has two beta-sheets and two alpha helices, including an IgG-like fold. The C-terminal domain exhibits 3 beta-sheets and an Ig-like fold and high structural similarity to HSV1 gE. Conclusions: gE from VZV-infected cells elicits a human antibody response with a preference for the gI binding domain of gE. These results yield insights to VZV gE structure and immunogenicity, provide a framework for future studies, and may guide the design of additional herpesvirus vaccine antigens. Teaser: Structures of varicella zoster virus glycoprotein E reveal distinct antigenic domains and define epitopes for vaccine-elicited human antibodies.

Spectrum aided vision enhancer enhances mucosal visualization by hyperspectral imaging in capsule endoscopy.

Narrow-band imaging (NBI) is more efficient in detecting early gastrointestinal cancer than white light imaging (WLI). NBI technology is available only in conventional endoscopy, but unavailable in magnetic-assisted capsule endoscopy (MACE) systems due to MACE's small size and obstacles in image processing issues. MACE is an easy, safe, and convenient tool for both patients and physicians to avoid the disadvantages of conventional endoscopy. Enabling NBI technology in MACE is mandatory. We developed a novel method to improve mucosal visualization using hyperspectral imaging (HSI) known as Spectrum Aided Visual Enhancer (SAVE, Transfer N,Hitspectra Intelligent Technology Co., Kaohsiung, Taiwan). The technique was developed by converting the WLI image captured by MACE to enhance SAVE images. The structural similarity index metric (SSIM) between the WLI MACE images and the enhanced SAVE images was 91%, while the entropy difference between the WLI MACE images and the enhanced SAVE images was only 0.47%. SAVE algorithm can identify the mucosal break on the esophagogastric junction in patients with gastroesophageal reflux disorder. We successfully developed a novel image-enhancing technique, SAVE, in the MACE system, showing close similarity to the NBI from the conventional endoscopy system. The future application of this novel technology in the MACE system can be promising.

Implementation of machine learning models as a quantitative evaluation tool for preclinical studies in dental education.

Objective, valid, and reliable evaluations are needed in order to develop haptic skills in dental education. The aim of this study is to investigate the validity and reliability of the machine learning method in evaluating the haptic skills of dentistry students. One-hundred fifty 6th semester dental students have performed Class II amalgam (C2A) and composite resin restorations (C2CR), in which all stages were evaluated with Direct Observation Practical Skills forms. The final phase was graded by three trainers and supervisors separately. Standard photographs of the restorations in the final stage were taken from different angles in a special setup and transferred to the Python program which utilized the Structural Similarity algorithm to calculate both the quantitative (numerical) and qualitative (visual) differences of each restoration. The validity and reliability analyses of inter-examiner evaluation were tested by Cronbach's Alpha and Kappa statistics (p=0.05). The intra-examiner reliability between Structural Similarity Index (SSIM) and examiners was found highly reliable in both C2A (α=0.961) and C2CR (α=0.856). The compatibility of final grades given by SSIM (53.07) and examiners (56.85) was statistically insignificant (p>0.05). A significant difference was found between the examiners and SSIM when grading the occlusal surfaces in C2A and on the palatal surfaces of C2CR (p<0.05). The concordance of observer assessments was found almost perfect in C2A (κ=0.806), and acceptable in C2CR (κ=0.769). Although deep machine learning is a promising tool in the evaluation of haptic skills, further improvement and alignments are required for fully objective and reliable validation in all cases of dental training in restorative dentistry.

Assessment of image-derived input functions from small vessels for patlak parametric imaging using total-body PET/CT.

The image-derived input function (IDIF) from the descending aorta has demonstrated performance comparable to arterial blood sampling while avoiding its invasive nature in parametric imaging. However, in conventional PET, large vessels may not always be within the imaging field of view (FOV). This study aims to evaluate the efficacy of dynamic parametric Ki imaging using image-derived input functions (IDIFs) extracted from various arteries, facilitated by total-body PET/CT. Twenty-three participants underwent a 60-minute total-body [18F]FDG PET scan. Data from each subject were used to reconstruct both total-body PET images and short-axis field-of-view PET images at different bed positions, each with a 25cm axial field-of-view (AFOV). Partial volume correction (PVC) was performed using the blurred Van Cittert iterative deconvolution. IDIFs extracted from the descending aorta, carotid artery, abdominal aorta, and iliac artery were employed for Patlak analysis. The resulting Ki images were compared using quantification indicators and subjective assessment. Linear regression analysis was conducted to examine the correlation of Ki values among IDIFs in normal organ and lesion regions of interest (ROIs). High similarities were observed in Ki images derived from the IDIFs from the descending aorta and other arteries, with a median structural similarity index measure (SSIM) above 0.98 and a median peak signal-to-noise ratio (PSNR) above 37dB. Linear regression analysis revealed strong correlations in Ki values (r² > 0.88) between the descending aorta and the three alternative vessels, with slopes of the linear fits close to 1. No significant difference in lesion detectability among IDIFs was found, as assessed visually and using metrics such as tumor-to-background ratio (TBR) and contrast-to-noise ratio (CNR) (P < 0.05). IDIFs from smaller vessels can reliably reconstruct parametric Ki images without compromising lesion detectability, providing clinically relevant information.

Bioinspired coassembly of peptide, imidazolecarboxaldehyde, and copper for nanozyme with laccase-like activity for colorimetric detection of epinephrine

Epinephrine plays a vital role in the central nervous system, but its abnormal levels will result in various diseases. Therefore, facile, rapid and accurate detection of epinephrine is essential for clinical application. Due to its phenolic structure, epinephrine can be detected through colorimetric assays as it is oxidized by laccase to form a colored quinone compound. In this work, a laccase-like nanozyme, diphenylalanine peptide/4-imidazolecarboxaldehyde-Cu (FF/ICA-Cu), was designed to mimic the active center of natural laccase. The as-synthesized FF/ICA-Cu nanozyme showed high laccase-like activity because of its structural similarity to the natural laccase active center. Moreover, it exhibited excellent stability and recyclability, even under harsh conditions. Based on its laccase-like activity, a sensitive and selective sensing strategy for epinephrine was developed. This approach offers a promising avenue for accurately detecting epinephrine and designing catalysts with potential for future industrial applications.