Structural Similarity Research Articles

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.

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.

AI generated synthetic STIR of the lumbar spine from T1 and T2 MRI sequences trained with open-source algorithms.

To train and evaluate an open-source generative adversarial networks (GANs) to create synthetic lumbar spine MRI STIR volumes from T1 and T2 sequences, providing a proof-of-concept that could allow for faster MRI examinations. 1817 MRI examinations with sagittal T1, T2, and STIR sequences were accumulated and randomly divided into training, validation, and test sets. GANs were trained to create synthetic STIR volumes using the T1 and T2 volumes as inputs, optimized using the validation set, then applied to the test set. Acquired and synthetic test set volumes were independently evaluated in a blinded, randomized fashion by three radiologists specializing in musculoskeletal imaging and neuroradiology. Readers assessed image quality, motion artifacts, perceived likelihood of the volume being acquired or synthetic, and presence of 7 pathologies. The optimal model leveraged a customized loss function that accentuated foreground pixels, achieving a structural similarity imaging metric (SSIM) of 0.842, mean absolute error (MAE) of 0.028, and peak signal to noise ratio (PSNR) of 26.367. Radiologists could distinguish synthetic from acquired volumes; however, the synthetic volumes were of equal or better quality in 77% of test patients and demonstrated equivalent or decreased motion artifacts in 78% of test patients. For common pathologies, the synthetic volumes had high positive predictive value (75-100%) but lower sensitivity (0-67%). This work links objective computer vision performance metrics and subject clinical evaluation of synthetic spine MRIs using open-source and reproducible methodologies. High-quality synthetic volumes are generated, reproducing many important pathologies, demonstrating a potential means for expediting imaging protocols. AI = Artificial Intelligence; GANs = general adversarial networks; aqSTIR = acquired STIR volume; sSTIR = synthetically generated STIR volume; SSIM = structural similarity imaging metric; PSNR = peak signal to noise ratio; MAE = mean absolute error.

A combinatory approach of non-chain ring and henon map for image encryption application

Algebraic structures play a vital role in securing important data. These structures are utilized to construct the non-linear components of block ciphers. Since constructing non-linear components through algebraic structures is crucial for the confusion aspects of encryption schemes, relying solely on these structures can result in limited key spaces. To address this issue, a complex non-chain ring with combination of Henon map based algorithm is presented. This proposed work aims to enhance both security and key space by utilizing complex algebraic structures. In this study, we investigate the use of unit elements from non-chain rings, establishing a mapping from the group of units to Galois fields to enhance the algebraic robustness of the scheme. A substitution box is constructed from Galois field and is applied in image processing. For pixel mixing, the Henon map is employed, and the exclusive OR operation is performed using a key generated from image pixels and the substitution box. The algorithm’s performance is evaluated using standard parameters such as histograms, Structural Similarity Index Measure (SSIM), correlation analysis, and National Institute of Standards and Technology (NIST) tests. For S-box analysis, we apply the strict avalanche criterion (SAC), bit independence criterion (BIC), linear approximation probability (LAP), and differential approximation probability (DAP). This algebraic approach utilizes a robust and state-of-the-art image encryption system that is highly effective against potential attackers.

Characterization of a chitinase from Trichinella spiralis and its immunomodulatory effects on allergic airway inflammation in mice

Abstract Background A fundamental tenet of the hygiene theory is the inverse association between helminth infections and the emergence of immune-mediated diseases. Research has been done to clarify the processes by which helminth-derived molecules can inhibit immunological disorders. This study aimed to evaluate the ability of Trichinella spiralis chitinase (Ts-chit) to ameliorate the symptoms of allergic airway inflammation. Methods Recombinant Trichinella spiralis chitinase (rTs-chit) was expressed in Escherichia coli BL21, and its structural homology to murine acidic mammalian chitinase (AMCase) was comprehensively analyzed. The expression of Ts-chit was examined across all T. spiralis life stages. To explore its immunomodulatory potential, a murine model of allergen-induced airway inflammation was established. The effects of rTs-chit were evaluated by assessing airway hyperresponsiveness and cytokine profiles in bronchoalveolar lavage fluid and performing detailed histopathological and immunohistochemical analyses. Results Recombinant Ts-chit (rTs-chit) was successfully expressed in E. coli BL21, showing strong structural similarity to murine acidic mammalian chitinase (AMCase). Expression profiling revealed that Ts-chit is present throughout all stages of the T. spiralis life cycle. In an allergic airway inflammation model, rTs-chit reduced weight loss and lung inflammation, lowering inflammatory cell infiltration and Th2 cytokines (IL-4, IL-5, IL-13) while increasing the immunosuppressive cytokine IL-10. Additionally, rTs-chit treatment decreased the expression of GATA3, arginase-1, MCP-1, CCL-11, and AMCase, along with reducing OVA-specific IgE, IgG, and IgG1 levels, suggesting its potential as an immunomodulatory agent. Conclusions This study highlights rTs-chit’s potential as a therapeutic agent for allergic airway diseases, leveraging its structural similarity to host chitinases to regulate Th2 responses and inflammatory pathways. The findings provide new insights into helminth-derived proteins as promising candidates for immune-based therapies. Graphical Abstract

Polyp image segmentation based on improved planet optimization algorithm using reptile search algorithm

Abstract To recognize the potential for colon polyps to develop into cancer over time, early diagnosis is crucial for preventative healthcare. Timely identification significantly improves the prognosis and treatment outcomes for colorectal cancer patients. Image segmentation is crucial in medical image analysis for accurate diagnosis and treatment planning. Therefore, in this study, we present an alternative multilevel thresholding polyp segmentation method (MPOA) to enhance the segmentation of polyp images. The proposed method is based on enhancing the planet optimization algorithm (POA) by integrating operators from the reptile search algorithm (RSA). The evaluation of the developed MPOA is tested with different polyp images and compared with other image segmentation approaches. The results highlight the superior capability of MPOA, as evidenced by various performance measures in effectively segmenting polyp images. Furthermore, metrics such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and fitness values demonstrate that MPOA outperforms the basic version of POA and other methods. The evaluation outcomes underscore the significant impact of RSA in enhancing the performance of POA for the segmentation of polyp images.

ADAR Therapeutics as a New Tool for Personalized Medicine

In the field of RNA therapy, innovative approaches based on adenosine deaminases acting on RNA (ADAR)-mediated site-directed RNA editing (SDRE) have been established, providing an exciting opportunity for RNA therapeutics. ADAR1 and ADAR2 enzymes are accountable for the predominant form of RNA editing in humans, which involves the hydrolytic deamination of adenosine (A) to inosine (I). This inosine is subsequently interpreted as guanosine (G) by the translational and splicing machinery because of their structural similarity. Intriguingly, the novel SDRE system leverages this recoding ability of ADAR proteins to correct the pathogenic G to A nucleotide mutations through a short, engineered guide RNA (gRNA). Thus, ADAR-mediated SDRE is emerging as a powerful tool to manipulate the genetic information at the RNA level and correct disease-causing mutations without causing damage to the genome. Further it is emerging as a new instrument for personalized medicine, since treatments can be tailored to the unique genetic mutations present in an individual patient. In this short review, we aimed to described the main approached bases on ADARs activity, highlighting their advantages and disadvantages.

Transcriptomic analysis reveals the crucial role of YABBY genes family in hormonal induced parthenocarpy in Cucumis sativus L.

BackgroundThe plant-specific YABBY transcription factor family plays several activities, including responding to abiotic stress, establishing dorsoventral polarity, and developing lateral organs. Cucumis sativus L. commonly referred to as cucumber and one of the first vegetable crops with a fully sequenced genome.ResultsIn this work, we examined the application of NAA, CPPU, and GA4 + 7 to inflict parthenocarpy in the cucumber ZK line. The expression pattern of YABBY genes throughout fruit development and performed a genomic census of cucumber (Cucumis sativus L.). Based on peptide classification, we discovered eight CsYABBY genes and divided them into five subfamilies. Similarities in motif composition and exon-intron structure were also observed. The cis-elements and gene ontology (GO) analysis revealed the involvement of CsYABBY genes in vegetative growth and the transition of vegetative to the reproductive phase. The expression analysis revealed the differential expression response to NAA, CPPU, and GA4 + 7. In particular, the CsYABBY1 was induced sharply by NAA and CPPU but not GA4 + 7. The transient expression of CsCRC disclosed that it is localized in the nucleus.ConclusionThese findings point to the possibility that CsYABBY1 and CsCRC may positively affect fruit development and could be utilized to generate parthenocarpic cucumber fruits.

Generation of high-resolution MPRAGE-like images from 3D head MRI localizer (AutoAlign Head) images using a deep learning-based model.

Magnetization prepared rapid gradient echo (MPRAGE) is a useful three-dimensional (3D) T1-weighted sequence, but is not a priority in routine brain examinations. We hypothesized that converting 3D MRI localizer (AutoAlign Head) images to MPRAGE-like images with deep learning (DL) would be beneficial for diagnosing and researching dementia and neurodegenerative diseases. We aimed to establish and evaluate a DL-based model for generating MPRAGE-like images from MRI localizers. Brain MRI examinations including MPRAGE taken at a single institution for investigation of mild cognitive impairment, dementia and epilepsy between January 2020 and December 2022 were included retrospectively. Images taken in 2020 or 2021 were assigned to training and validation datasets, and images from 2022 were used for the test dataset. Using the training and validation set, we determined one model using visual evaluation by radiologists with reference to image quality metrics of peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and Learned Perceptual Image Patch Similarity (LPIPS). The test dataset was evaluated by visual assessment and quality metrics. Voxel-based morphometric analysis was also performed, and we evaluated Dice score and volume differences between generated and original images of major structures were calculated as absolute symmetrized percent change. Training, validation, and test datasets comprised 340 patients (mean age, 56.1 ± 24.4years; 195 women), 36 patients (67.3 ± 18.3years, 20 women), and 193 patients (59.5 ± 24.4years; 111 women), respectively. The test dataset showed: PSNR, 35.4 ± 4.91; SSIM, 0.871 ± 0.058; and LPIPS 0.045 ± 0.017. No overfitting was observed. Dice scores for the segmentation of main structures ranged from 0.788 (left amygdala) to 0.926 (left ventricle). Quadratic weighted Cohen kappa values of visual score for medial temporal lobe between original and generated images were 0.80-0.88. Images generated using our DL-based model can be used for post-processing and visual evaluation of medial temporal lobe atrophy.

Insulin and myometrial contractility; Are there any links? A narrative review.

Contrary to the evidence supporting the role for insulin in stimulating uterine contraction, only a limited number of studies have highlighted the inhibitory effect of insulin on myometrial contractions in human and rodent. A hypothetical narrative review of the current literature was conducted, revealing the current literature and shows the potential inhibitory effects of insulin on myometrial contractility. These inhibitory mechanisms include activation of adenylyl cyclase signaling pathways, an increase in cAMP production, a decrease in Ca2 + influx and cytosolic Ca2+, hyperpolarization of the cell membrane, and stimulation of NO synthesis. Altered oxytocin sensitivity, structural similarity to relaxin, modulating abscisic acid (ABA) effect, and synergistic interaction with progesterone, adiponectin, and leptin may also represent additional mechanisms for the inhibitory effects of insulin on myometrial contractions. The literature indicates that insulin exhibits inhibitory effects on myometrial contractility. Confirming such a conclusion through future studies may propose insulin as a possible uterine quiescent.