Structural Similarity Research Articles

A low-dose CT reconstruction method using sub-pixel anisotropic diffusion.

We present a new low-dose CT reconstruction method using sub-pixel and anisotropic diffusion. The sub-pixel intensity values and their second-order differences were obtained using linear interpolation techniques, and the new gradient information was then embedded into an anisotropic diffusion process, which was introduced into a penalty-weighted least squares model to reduce the noise in low-dose CT projection data. The high-quality CT image was finally reconstructed using the classical filtered back-projection (FBP) algorithm from the estimated data. In the Shepp-Logan phantom experiments, the structural similarity (SSIM) index of the CT image reconstructed by the proposed algorithm, as compared with FBP, PWLS-Gibbs and PWLS-TV algorithms, was increased by 28.13%, 5.49%, and 0.91%, the feature similarity (FSIM) index was increased by 21.08%, 1.78%, and 1.36%, and the root mean square error (RMSE) was reduced by 69.59%, 18.96%, and 3.90%, respectively. In the digital XCAT phantom experiments, the SSIM index of the CT image reconstructed by the proposed algorithm, as compared with FBP, PWLS-Gibbs and PWLS-TV algorithms, was increased by 14.24%, 1.43% and 7.89%, the FSIM index was increased by 9.61%, 1.78% and 5.66%, and the RMSE was reduced by 26.88%, 9.41% and 18.39%, respectively. In clinical experiments, the SSIM index of the image reconstructed using the proposed algorithm was increased by 19.24%, 15.63% and 3.68%, the FSIM index was increased by 4.30%, 2.92% and 0.43%, and the RMSE was reduced by 44.60%, 36.84% and 15.22% in comparison with FBP, PWLS-Gibbs and PWLS-TV algorithms, respectively. The proposed method can effectively reduce the noises and artifacts while maintaining the structural details in low-dose CT images.

PE-CycleGAN network based CBCT-sCT generation for nasopharyngeal carsinoma adaptive radiotherapy.

To explore the synthesis of high-quality CT (sCT) from cone-beam CT (CBCT) using PE-CycleGAN for adaptive radiotherapy (ART) for nasopharyngeal carcinoma. A perception-enhanced CycleGAN model "PE-CycleGAN" was proposed, introducing dual-contrast discriminator loss, multi-perceptual generator loss, and improved U-Net structure. CBCT and CT data from 80 nasopharyngeal carcinoma patients were used as the training set, with 7 cases as the test set. By quantifying the mean absolute error (MAE), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), as well as the dose gamma pass rate and the relative dose deviations of the target area and organs at risk (OAR) between sCT and reference CT, the image quality and dose calculation accuracy of sCT were evaluated. The MAE of sCT generated by PE-CycleGAN compared to the reference CT was (56.89±13.84) HU, approximately 30% lower than CBCT's (81.06±15.86) HU (P<0.001). PE-CycleGAN's PSNR and SSIM were 26.69±2.41dB and 0.92±0.02 respectively, significantly higher than CBCT's 21.54±2.37dB and 0.86±0.05 (P<0.001), indicating substantial improvements in image quality and structural similarity. In gamma analysis, under the 2 mm/2% criterion, PE-CycleGAN's sCT achieved a pass rate of (90.13±3.75)%, significantly higher than CBCT's (81.65±3.92)% (P<0.001) and CycleGAN's (87.69±3.50)% (P<0.05). Under the 3 mm/3% criterion, PE-CycleGAN's sCT pass rate of (90.13±3.75)% was also significantly superior to CBCT's (86.92±3.51)% (P<0.001) and CycleGAN's (94.58±2.23)% (P<0.01). The mean relative dose deviation of the target area and OAR between sCT and planned CT was within ±3% for all regions, except for the Lens Dmax (Gy), which had a deviation of 3.38% (P=0.09). The mean relative dose deviations for PTVnx HI, PTVnd HI, PTVnd CI, PTV1 HI, PRV_SC, PRV_BS, Parotid, Larynx, Oral, Mandible, and PRV_ON were all less than ±1% (P>0.05). PE-CycleGAN demonstrates the ability to rapidly synthesize high-quality sCT from CBCT, offering a promising approach for CBCT-guided adaptive radiotherapy in nasopharyngeal carcinoma.

A Novel Computational Machine Learning Pipeline to Quantify Similarities in Three-Dimensional Protein Structures.

Animal models are widely used during drug development. The selection of suitable animal model relies on various factors such as target biology, animal resource availability and legacy species. It is imperative that the selected animal species exhibit the highest resemblance to human, in terms of target biology as well as the similarity in the target protein. The current practice to address cross-species protein similarity relies on pair-wise sequence comparison using protein sequences, instead of the biologically relevant 3-dimensional (3D) structure of proteins. We developed a novel quantitative machine learning pipeline using 3D structure-based feature data from the Protein Data Bank, nominal data from UNIPROT and bioactivity data from ChEMBL, all of which were matched for human and animal data. Using the XGBoost regression model, similarity scores between targets were calculated and based on these scores, the best animal species for a target was identified. For real-world application, targets from an alternative source, ie, AlphaFold, were tested using the model, and the animal species that had the most similar protein to the human counterparts were predicted. These targets were then grouped based on their associated phenotype such that the pipeline could predict an optimal animal species.

Utility of 3D facial reconstruction for forensic identification: a focus on facial soft tissue thickness and customized techniques.

Facial reconstruction, a crucial method in forensic identification, finds particular significance in cases where conventional means of identification are unavailable. This study addresses a significant gap in the field of forensic facial reconstruction focusing on facial soft tissue thickness (FSTT) and facial reconstruction techniques specifically tailored to the Thai population. By developing and implementing the 3D (three-dimensional) facial reconstruction program and compiling an extensive dataset of FSTT, this research makes substantial progress in advancing forensic facial reconstruction methodologies employing the combination Manchester Method, 3D skull images obtained through cone beam computed tomography (CBCT) scans were reconstructed using Autodesk Maya software. A dataset comprising 100 Thai cadavers underwent FSTT measurements via ultrasound (US) for 53 landmarks, with subsequent facial comparisons of 10 samples between reconstructed faces and real photographs conducted using the facial pool comparison and the structural similarity index (SSIM). The accuracy of facial pool comparison ranged from 30 to 80%, reflecting a wide range due to human errors. Thus, incorporating computerized assessment is necessary to minimize human bias. SSIM values ranged from 0.76 to 0.89, indicating strong similarity between reconstructed and real faces and validating the reconstruction process's accuracy. These findings suggest that the facial soft tissue thickness database of the Thai population used in this study can effectively support 3D computerized facial reconstruction. Moreover, this study sets the stage for future advancements in facial reconstruction methodologies tailored to diverse populations, emphasizing the ongoing need for comprehensive data gathering and technique refinement to enhance accuracy and applicability in forensic investigations.

A comparative analysis of image harmonization techniques in mitigating differences in CT acquisition and reconstruction.

The study aims to systematically characterize the effect of CT parameter variations on images and lung radiomic and deep features, and to evaluate the ability of different image harmonization methods to mitigate the observed variations. A retrospective in-house sinogram dataset of 100 low-dose chest CT scans was reconstructed by varying radiation dose (100%, 25%, 10%) and reconstruction kernels (smooth, medium, sharp). A set of image processing, convolutional neural network (CNNs), and generative adversarial network-based (GANs) methods were trained to harmonize all image conditions to a reference condition (100% dose, medium kernel). Harmonized scans were evaluated for image similarity using peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and learned perceptual image patch similarity (LPIPS), and for the reproducibility of radiomic and deep features using concordance correlation coefficient (CCC). CNNs consistently yielded higher image similarity metrics amongst others; for Sharp/10%, which exhibited the poorest visual similarity, PSNR increased from a mean ± CI of 17.763 ± 0.492 to 31.925 ± 0.571, SSIM from 0.219 ± 0.009 to 0.754 ± 0.017, and LPIPS decreased from 0.490 ± 0.005 to 0.275 ± 0.016. Texture-based radiomic features exhibited a greater degree of variability across conditions, i.e. a CCC of 0.500 ± 0.332, compared to intensity-based features (0.972 ± 0.045). GANs achieved the highest CCC (0.969 ± 0.009 for radiomic and 0.841 ± 0.070 for deep features) amongst others. Convolutional neural networks are suitable if downstream applications necessitate visual interpretation of images, whereas generative adversarial networks are better alternatives for generating reproducible quantitative image features needed for machine learning applications. Understanding the efficacy of harmonization in addressing multi-parameter variability is crucial for optimizing diagnostic accuracy and a critical step toward building generalizable models suitable for clinical use.

TagGen: Diffusion-based generative model for cardiac MR tagging super resolution.

The aim of the work is to develop a cascaded diffusion-based super-resolution model for low-resolution (LR) MR tagging acquisitions, which is integrated with parallel imaging to achieve highly accelerated MR tagging while enhancing the tag grid quality of low-resolution images. We introduced TagGen, a diffusion-based conditional generative model that uses low-resolution MR tagging images as guidance to generate corresponding high-resolution tagging images. The model was developed on 50 patients with long-axis-view, high-resolution tagging acquisitions. During training, we retrospectively synthesized LR tagging images using an undersampling rate (R) of 3.3 with truncated outer phase-encoding lines. During inference, we evaluated the performance of TagGen and compared it with REGAIN, a generative adversarial network-based super-resolution model that was previously applied to MR tagging. In addition, we prospectively acquired data from 6 subjects with three heartbeats per slice using 10-fold acceleration achieved by combining low-resolution R = 3.3 with GRAPPA-3 (generalized autocalibrating partially parallel acquisitions 3). For synthetic data (R = 3.3), TagGen outperformed REGAIN in terms of normalized root mean square error, peak signal-to-noise ratio, and structural similarity index (p < 0.05 for all). For prospectively 10-fold accelerated data, TagGen provided better tag grid quality, signal-to-noise ratio, and overall image quality than REGAIN, as scored by two (blinded) radiologists (p < 0.05 for all). We developed a diffusion-based generative super-resolution model for MR tagging images and demonstrated its potential to integrate with parallel imaging to reconstruct highly accelerated cine MR tagging images acquired in three heartbeats with enhanced tag grid quality.

Characterization for the Similarity Assessment Between the Proposed Biosimilar SB17 and Ustekinumab Reference Product Using State-of-the-Art Analytical Methods.

SB17 is being developed as a biosimilar to ustekinumab reference product (RP), a human monoclonal antibody (IgG1 kappa immunoglobulin) that binds to the common p40 subunit of cytokines interleukin (IL)-23 and IL-12. Binding to this subunit prevents interaction with their receptor, resulting in modulation in the immune system responses that play a key role in inflammatory disease. The objective of this study was to demonstrate structural, physicochemical, and biological similarity between ustekinumab RP and SB17 using various state-of-the-art analytical methods. Comprehensive analytical characterization was conducted by the quality range and side-by-side comparison approach for SB17 versus the European Union (EU) and US ustekinumab RP using various analytical methods. Comparisons included purity, product-related impurity, charge heterogeneity, primary structure, posttranslational modification, higher-order structure, quantity, Fab-related biological activities (potency and binding activity), and Fc-related biological activities. On the basis of the analytical similarity assessment, the structural, physicochemical, and biological characterization results demonstrated that SB17 is comparable to the ustekinumab RP. In the structural aspects, it was confirmed that there is no clinically meaningful difference between posttranslational modification profiles and higher-order structures of SB17 compared with the ustekinumab RP. Product-related impurities in the form of aggregates were also confirmed to be similar. SB17 has lower acidic and basic variants compared with ustekinumab RP, owing to the difference in the producing cell line. Ustekinumab RP is expressed in an Sp2/0 cell line, whereas SB17 is expressed in CHO cell line. However, the difference between SB17 and ustekinumab RP in the charge variants is not considered to be clinically meaningful, since equivalent biological activity was observed. In the case of mechanism of action (MoA)-related biological activities, SB17 is highly similar to the EU and US ustekinumab RP with respect to overall critical and noncritical quality attributes analyzed. Moreover, similarity or lack of activity in Fc-related biological activities was also confirmed. On the basis of these results, SB17 is expected to have comparable safety and efficacy as compared with ustekinumab RP. In summary, the overall analytical characterization and similarity assessment results show that SB17 is comparable to the EU and US ustekinumab RP in terms of structural, physicochemical, biophysical, and biological attributes.

Synthetic [18F]FET-PET Generation and Hotspot Prediction Via Preoperative MRI Fusion of Glioma Lacking Radiographic High-Grade Characteristics

Abstract Background Limited amino acid availability for Positron Emission Tomography (PET) imaging hinders therapeutic decision-making for gliomas without typical high-grade imaging features. To address this gap, we evaluated a generative artificial intelligence (AI) approach for creating synthetic [18F]FET-PET and predicting high [18F]FET-uptake from magnetic resonance imaging (MRI). Methods We trained a deep learning (DL)-based model to segment tumors in MRI and extracted radiomic features using the pyradiomics package and classification with a Random Forest classifier. To generate [18F]FET-PET images, we employed a Generative Adversarial Network (GAN) framework and utilized a split-input fusion module for processing different MRI sequences through feature extraction, concatenation, and self-attention. Results We included MR and PET images from 215 studies for the hotspot classification and 211 studies for the synthetic PET generation task. The top-performing radiomic features achieved 80% accuracy for hotspot prediction. From the synthetic [18F]FET-PET, 85% were classified as clinically useful by senior physicians. Peak Signal-to-Noise Ratio analysis indicated high signal fidelity with a peak at 40 dB, while Structural Similarity Index values showed structural congruence. Root Mean Square Error analysis demonstrated lower values below 5.6. Most Visual Information Fidelity scores ranged between 0.6 and 0.7. This indicates that synthetic PET images retain the essential information required for clinical assessment and diagnosis. Conclusion For the first time, we demonstrate that predicting high [18F]FET-uptake and generating synthetic PET images from preoperative MRI in LGG and HGG is feasible. Advanced MRI modalities and other generative AI models will be used to improve the algorithm further in future studies.

Remote sensing image Super-resolution reconstruction by fusing multi-scale receptive fields and hybrid transformer

To enhance high-frequency perceptual information and texture details in remote sensing images and address the challenges of super-resolution reconstruction algorithms during training, particularly the issue of missing details, this paper proposes an improved remote sensing image super-resolution reconstruction model. The generator network of the model employs multi-scale convolutional kernels to extract image features and utilizes a multi-head self-attention mechanism to dynamically fuse these features, significantly improving the ability to capture both fine details and global information in remote sensing images. Additionally, the model introduces a multi-stage Hybrid Transformer structure, which processes features at different resolutions progressively, from low resolution to high resolution, substantially enhancing reconstruction quality and detail recovery. The discriminator combines multi-scale convolution, global Transformer, and hierarchical feature discriminators, providing a comprehensive and refined evaluation of image quality. Finally, the model incorporates a Charbonnier loss function and total variation (TV) loss function, which significantly improve training stability and accelerate convergence. Experimental results demonstrate that the proposed method, compared to the SRGAN algorithm, achieves average improvements of approximately 3.61 dB in Peak Signal-to-Noise Ratio (PSNR), 0.070 (8.2%) in Structural Similarity Index (SSIM), and 0.030 (3.1%) in Feature Similarity Index (FSIM) across multiple datasets, showing significant performance gains.

Structural basis of Spliced Leader RNA recognition by the Trypanosoma brucei cap-binding complex

Kinetoplastids are a clade of eukaryotic protozoans that include human parasitic pathogens like trypanosomes and Leishmania species. In these organisms, protein-coding genes are transcribed as polycistronic pre-mRNAs, which need to be processed by the coupled action of trans-splicing and polyadenylation to yield monogenic mature mRNAs. During trans-splicing, a universal RNA sequence, the spliced leader RNA (SL RNA) mini-exon, is added to the 5’-end of each mRNA. The 5’-end of this mini-exon carries a hypermethylated cap structure and is bound by a trypanosomatid-specific cap-binding complex (CBC). The function of three of the kinetoplastid CBC subunits is unknown, but an essential role in cap-binding and trans-splicing has been suggested. Here, we report cryo-EM structures that reveal the molecular architecture of the Trypanosoma brucei CBC (TbCBC) complex. We find that TbCBC interacts with two distinct features of the SL RNA. The TbCBP20 subunit interacts with the m7G cap while TbCBP66 recognizes double-stranded portions of the SL RNA. Our findings pave the way for future research on mRNA maturation in kinetoplastids. Moreover, the observed structural similarities and differences between TbCBC and the mammalian cap-binding complex will be crucial for considering the potential of TbCBC as a target for anti-trypanosomatid drug development.

Grounding Grid Electrical Impedance Imaging Method Based on an Improved Conditional Generative Adversarial Network

The grounding grid is an important piece of equipment to ensure the safety of a power system, and thus research detecting on its corrosion status is of great significance. Electrical impedance tomography (EIT) is an effective method for grounding grid corrosion imaging. However, the inverse process of image reconstruction has pathological solutions, which lead to unstable imaging results. This paper proposes a grounding grid electrical impedance imaging method based on an improved conditional generative adversarial network (CGAN), aiming to improve imaging precision and accuracy. Its generator combines a preprocessing module and a U-Net model with a convolutional block attention module (CBAM). The discriminator adopts a PatchGAN structure. First, a grounding grid forward problem model was built to calculate the boundary voltage. Then, the image was initialized through the preprocessing module, and the important features of ground grid corrosion were extracted again through the encoder module, decoder module and attention module. Finally, the generator and discriminator continuously optimized the objective function and conducted adversarial training to achieve ground grid electrical impedance imaging. Imaging was performed on grounding grids with different corrosion conditions. The results showed a final average peak signal-to-noise ratio of 20.04. The average structural similarity was 0.901. The accuracy of corrosion position judgment was 94.3%. The error of corrosion degree judgment was 9.8%. This method effectively improves the pathological problem of grounding grid imaging and improves the precision and accuracy, with certain noise resistance and universality.

Anatomical and palynological peculiarities of rare species Hyacinthella atropatana (Hyacinthaceae) in Armenia

Hyacinthella atropatana (Hyacinthaceae) is endemic to South Transcaucasia and is also listed in the Red Book of Plants of Armenia under the EN (Endangered) category. There are three known locations of this species, namely the Darelegis and Meghri floristic regions (Armenia) and Nakhchivan (Azerbaijan). The comparative anatomical analysis of H. atropatana specimens from both Armenian populations was conducted for the first time. The results revealed no anatomical differences between the examined specimens. The observed anatomical features, including the mechanical tissue in the scape and palisade tissue in the leaf, underscore the species’ xerophilic adaptations. Our research contributes also novel quantitative data to supplement existing literature. An evaluation of the species’ anatomical traits in relation to other investigated Hyacinthella and Scilla species substantiates H. atropatana’s closest structural similarity to the genus Hyacinthella, particularly regarding scape and leaf anatomy. At the same time, H. atropatana was distinguished from the representatives of both genera by having the smallest number of vascular bundles in both the scape and the leaf, as well as a smaller number of cortex layers in the scape. The data obtained confirmed the previous opinion on the uniqueness of this species. A comprehensive study of the pollen characteristics of H. atropatana was conducted also for the first time. It revealed a noticeable palynomorphological similarity between the two Armenian populations, as well as between samples collected in Armenia and Nakhchivan (Azerbaijan), which may be attributed to the considerable resemblance of their habitats. Additionally, our research established that within the Hyacinthella genus, H. atropatana is distinctive both in the size of pollen grains (somewhat larger than those noted for other studied species) and the uniqueness of pollen exine ornamentation. The data received support the previously proposed assumption about some isolation of this species within the Hyacinthella genus and its placement in a separate section, Atropatana.

W-Shaped Net: An Inter-Slice Super-Resolution Segmentation Deep Network for CT Scans of Hepatic Ducts

The three-dimensional (3D) reconstruction of the hepatic duct tree is significant for the minimally invasive surgery of hepatobiliary stone disease, which can be influenced by the quantity of the CT scans of hepatic ducts. If insufficient CT scans with low inter-slice resolution are directly utilized for 3D reconstruction, discontinuities and gaps will emerge in the reconstructed hepatic duct tree. In this paper, a novel end-to-end deep learning framework is designed for the inter-slice super-resolution segmentation of the CT slices of hepatic ducts, which can improve the 3D reconstruction performance in the inter-slice dimension. Specifically, the framework cascades into an inter-slice super-resolution subnetwork and a segmentation subnetwork. A deep learning network is introduced as the inter-slice super-resolution subnetwork to generate an intermediate slice between two adjacent CT slices in the simulated CT scans with low inter-slice resolution. To capture the spatiotemporal correlation existing in the CT scans of hepatic ducts, the ConvLSTM is introduced into the U-Net-like segmentation subnetwork in the high-dimensional feature space. To further suppress the problems of discontinuities and gaps, a structure-aware loss function is proposed by incorporating the structural similarity index measure (SSIM) as a regulator to dynamically assign the contribution of the generated CT slice to the total loss of the designed framework. Experimental results demonstrate that our proposed framework performs better segmentations for hepatic ducts than several existing deep learning networks with the performance of 0.7690 DICE and a 0.7712 F1-score, which is beneficial for the 3D reconstruction of the hepatic duct tree.

Structural considerations and differences between leaf canals and secretory cavities in Asteraceae.

Secretory canals are distributed among seed plants, and their diversity is concentrated in many families of angiosperms, while other internal secretory structures such as secretory cavities have been identified only in Rutaceae, Myrtaceae, and Asteraceae. Identifying and recognizing these two types of secretory structures has been complicated, mainly due to their structural similarities. In this study, the ontogeny of canals and secretory cavities in two species of Asteraceae are described and compared, to understand the structural differences between them and allow the establishment of more appropriate homology hypotheses. Leaves of Bidens odorata and Tagetes tenuifolia in different stages of development, including the apex of the stems, were collected. The samples were processed using the methacrylate technique, and longitudinal and transverse sections were made. The development of both, canals and secretory cavities, is schizogenous, in contrast to what was previously reported for other families such as Rutaceae, where they are reported as lysigenous. In Asteraceae, canals originate from cells of the procambium while cavities originate from cells of the ground meristem. The structural and developmental similarities between both types of secretory structures allow us to infer that they have a close evolutionary origin. Canals and secretory cavities in Asteraceae can be differentiated based on the number of strata of secretory epithelium and sheath, the modifications of epidermal cells and mesophyll, and the type of promeristem that gives rise to them. Probably extravascular canals give rise to cavities in leaves of Asteraceae and probably in other plant families.

New Oxygenated Methoxy-p-Cymene Derivatives from Leopard’s Bane (Doronicum columnae Ten., Asteraceae) Essential Oil: Synthesis Facilitating the Identification of Isomeric Minor Constituents in Complex Matrices

Species of the genus Doronicum are known for their pharmacological properties and essential oils, the chemical composition of which remains inadequately studied. In this work, GC-MS analysis, synthesis, and spectral techniques (UV, IR, MS, and NMR) were employed to identify 83 constituents in the essential oil from D. columnae roots, which accounted for 98.1% of the total GC-peak area. The major components were thymyl isobutyrate (32.8%) and thymyl 2-methylbutyrate (22.8%), while the minor constituents were methoxy-p-cymene derivatives. Six new natural products were identified through synthesis, GC co-injection experiments and spectral characterization, including esters (isobutyrate, 2-methylbutyrate, and/or isovalerate) of 2-methoxycuminol, 6-methoxythymol, and 6-hydroxythymyl methyl ether, as well as methyl 3-methoxycuminate. Their identification was made possible by synthesis efforts, as isolating pure compounds was impracticable because of their low abundance and the overall structural similarity within the highly complex mixture that was the essential oil.

Absorptivity Is an Important Determinant in the Toxicity Difference between Aristolochic Acid I and Aristolochic Acid II.

Inadvertent exposure to aristolochic acids (AAs) is causing chronic renal disease worldwide, with aristolochic acid I (AA-I) identified as the primary toxic agent. This study employed chemical methods to investigate the mechanisms underlying the nephrotoxicity and carcinogenicity of AA-I. Aristolochic acid II (AA-II), which has a structure similar to that of AA-I, was investigated with the same methods for comparison. Despite their structural similarities, findings from cultured human cells and gut sac experiments showed that AA-I is absorbed more effectively than AA-II (∼3 times greater for AA-I than for AA-II; p < 0.001). This increased absorption, along with the previously observed higher activity of reductive activation enzymes for AA-I, results in greater DNA damage and oxidative stress, both of which are key factors in AA-related toxicity. The similar patterns of cell mortality (34.4 ± 2.3% vs 9.7 ± 0.1% for AA-I and AA-II at 80 μM; p < 0.0001), DNA adduct formation (∼3 times greater for AA-I than for AA-II; p < 0.001), and oxidative stress levels in relation to the concentrations of AA-I and AA-II indicate that the higher absorption rate of AA-I is a significant contributor to its greater toxicity. The toxicity of AA-I was also found to be further enhanced by its (natural) coexistence with AA-II. Since AA-I and AA-II differ only by a methoxy group, future research on reducing risks associated with AA exposure should focus on strategies to lower the absorption of these compounds.

AAPM Truth-based CT (TrueCT) reconstruction grand challenge.

This Special Report summarizes the 2022, AAPM grand challenge on Truth-based CT image reconstruction. To provide an objective framework for evaluating CT reconstruction methods using virtual imaging resources consisting of a library of simulated CT projection images of a population of human models with various diseases. Two hundred unique anthropomorphic, computational models were created with varied diseases consisting of 67 emphysema, 67 lung lesions, and 66 liver lesions. The organs were modeled based on clinical CT images of real patients. The emphysematous regions were modeled using segmentations from patient CT cases in the COPDGene Phase I dataset. For the lung and liver lesion cases, 1-6 malignant lesions were created and inserted into the human models, with lesion diameters ranging from 5.6 to 21.9mm for lung lesions and 3.9 to 14.9mm for liver lesions. The contrast defined between the liver lesions and liver parenchyma was 82±12 HU, ranging from 50 to 110 HU. Similarly, the contrast between the lung lesions and the lung parenchyma was defined as 781±11 HU, ranging from 725 to 805 HU. For the emphysematous regions, the defined HU values were -950±17 HU ranging from -918 to -979 HU. The developed human models were imaged with a validated CT simulator. The resulting CT sinograms were shared with the participants. The participants reconstructed CT images from the sinograms and sent back their reconstructed images. The reconstructed images were then scored by comparing the results against the corresponding ground truth values. The scores included both task-generic (root mean square error [RMSE] and structural similarity matrix [SSIM]), and task-specific (detectability index [d'] and lesion volume accuracy) metrics. For the cases with multiple lesions, the measured metric was averaged across all the lesions. To combine the metrics with each other, each metric was normalized to a range of 0 to 1 per disease type, with "0" and "1" being the worst and best measured values across all cases of the disease type for all received reconstructions. The True-CT challenge attracted 52 participants, out of which 5 successfully completed the challenge and submitted the requested 200 reconstructions. Across all participants and disease types, SSIM absolute values ranged from 0.22 to 0.90, RMSE from 77.6 to 490.5 HU, d' from 0.1 to 64.6, and volume accuracy ranged from 1.2 to 753.1mm3. The overall scores demonstrated that participant "A" had the best performance in all categories, except for the metrics of d' for lung lesions and RMSE for liver lesions. Participant "A" had an average normalized score of 0.41±0.22, 0.48±0.32, and 0.42±0.33 for the emphysema, lung lesion, and liver lesion cases, respectively. The True-CT challenge successfully enabled objective assessment of CT reconstructions with the unique advantage of access to a diverse population of diseased human models with known ground truth. This study highlights the significant potential of virtual imaging trials in objective assessment of medical imaging technologies.

Synergistic protection of nascent DNA at stalled forks by MSANTD4 and BRCA1/2-RAD51.

The regressed arms of reversed replication forks exhibit structural similarities to one-ended double-stranded breaks and need to be protected against uncontrolled nucleolytic degradation. Here, we identify MSANTD4 (Myb/SANT-like DNA-binding domain-containing protein 4), a functionally uncharacterized protein that uniquely counters the replication protein A (RPA)-Bloom (BLM)/Werner syndrome helicase (WRN)-DNA replication helicase/nuclease 2 (DNA2) complex to safeguard reversed replication forks from detrimental degradation, independently of the breast cancer susceptibility proteins (BRCA1/2)-DNA repair protein RAD51 pathway. MSANTD4 specifically interacts with the junctions between single-stranded DNA (ssDNA) and double-stranded DNA (dsDNA) in DNA substrates harboring a 3' overhang, which resemble the structural features of regressed arms processed by WRN-DNA2. This DNA-binding capability allows MSANTD4 to accumulate at reversed forks, strategically antagonizing the RPA-BLM/WRN-DNA2 complex by impeding its access to the ssDNA-dsDNA junction of the regressed arms. Loss of MSANTD4 exacerbates genome instability induced by replication stress in BRCA1/2-deficient cells. Our findings unveil a collaborative defense mechanism orchestrated by MSANTD4 and BRCA1/2-RAD51, effectively counteracting nucleolytic attacks on the regressed arms and synergistically preserving the integrity of reversed forks.

The Distribution, Structure, and Chemical Composition of Alkali-Silica Gels in Calcined Clay Concretes

This study investigates the effect of calcined clays (metakaolin, metasilt, metaclay) on the chemical composition, distribution, and structure of alkali–silica reaction (ASR) gels. Using 10 wt% of calcined clays reduced concrete expansion and minimized cracking but did not inhibit ASR gel formation. Micro X-ray fluorescence mapping revealed an average ASR gel content of 3 wt% in concrete, incorporating up to two-thirds of K2O and nearly all Na2O from the binder. Raman spectroscopy indicated structural similarities among gels in different concrete mixes, with an increased degree of polymerization in the metakaolin-containing concrete. Automated mineralogy identified four gel phases: Si gel, Ca-Si gel, Al-Ca-Si gel, and Al-Si gel. Ca-Si gels are formed at binder interfaces, while non-swellable Al-bearing gels are mainly formed in metakaolin-containing concrete located within aggregates. This study shows that aluminum can be incorporated into gels in calcined clay concretes, altering their structure and potentially affecting their expansion behavior in concrete.

Bioactive Sulfonamides Derived from Amino Acids: Their Synthesis and Pharmacological Activities.

Currently, the synthesis of bioactive sulfonamides using amino acid as a starting reagent has become an area of research interest in organic chemistry. Over the years, an amine-sulfonyl chloride reaction has been adopted as a common step in traditional sulfonamide synthetic methods. However, recent developments have shown amino acids to be better precursors than amines in the synthesis of sulfonamides. Although amines and amino acids have some structural similarities, using amino acids rather than amines in the synthesis of sulfonamides minimizes several drawbacks. Comparatively, amino acids are preferred to amines as starting reagents in sulfonamide synthesis due to their biological relevance, chirality, stereochemistry, diversity of side chains, orthogonality in functional group manipulation, the potential for peptide and protein synthesis, mild reaction conditions, alignment with green chemistry principles, diverse synthetic applications, easy availability, and economic viability. Amino acids, having the aforementioned properties, offer a versatile platform for the synthesis of sulfonamides with tailored structures. The reaction mechanism of the synthesis of amino acid-derived sulfonamides involves a nucleophilic attack by the amino group on the activated sulfonyl species to produce a sulfonamide functional group. Amino acid-based sulfonamides have numerous pharmacological activities, including antibacterial, antiviral, anticancer, antioxidant, anti-inflammatory, anti-plasmodial, antimalarial, anti-trypanosomal, and insect growth regulatory properties. This review discusses several synthetic processes, emphasizing established ways, cutting- edge techniques, and novel approaches that emphasize the significance of amino acids in the synthesis of sulfonamides. The structure-activity relationship of amino acid-derived sulfonamides and their pharmacological activities are also highlighted.