A Deep Learning Model for Automatically Quantifying the Anterior Segment in Ultrasound Biomicroscopy Images of Implantable Collamer Lens Candidates.

OBJECTIVE: This study aimed to develop and evaluate a deep learning-based model that could automatically measure anterior segment (AS) parameters on preoperative ultrasound biomicroscopy (UBM) images of implantable Collamer lens (ICL) surgery candidates. METHODS: A total of 1164 panoramic UBM images were preoperatively obtained from 321 patients who received ICL surgery in the Eye Center of Renmin Hospital of Wuhan University (Wuhan, China) to develop an imaging database. First, the UNet++ network was utilized to segment AS tissues automatically, such as corneal lens and iris. In addition, image processing techniques and geometric localization algorithms were developed to automatically identify the anatomical landmarks (ALs) of pupil diameter (PD), anterior chamber depth (ACD), angle-to-angle distance (ATA), and sulcus-to-sulcus distance (STS). Based on the results of the latter two processes, PD, ACD, ATA, and STS can be measured. Meanwhile, an external dataset of 294 images from Huangshi Aier Eye Hospital was employed to further assess the model's performance in other center. Lastly, a subset of 100 random images from the external test set was chosen to compare the performance of the model with senior experts. RESULTS: Whether in the internal test dataset or external test dataset, using manual labeling as the reference standard, the models achieved a mean Dice coefficient exceeding 0.880. Additionally, the intra-class correlation coefficients (ICCs) of ALs' coordinates were all greater than 0.947, and the percentage of Euclidean distance distribution of ALs within 250 µm was over 95.24%.While the ICCs for PD, ACD, ATA, and STS were greater than 0.957, furthermore, the average relative error (ARE) of PD, ACD, ATA, and STS were below 2.41%. In terms of human versus machine performance, the ICCs between the measurements performed by the model and those by senior experts were all greater than 0.931. CONCLUSION: A deep learning-based model could measure AS parameters using UBM images of ICL candidates, and exhibited a performance similar to that of a senior ophthalmologist.

Inhibiting PRMT1 protects against CoNV by regulating macrophages through the FGF2/PI3K/Akt pathway.

Corneal neovascularization (CoNV) is predominantly initiated by inflammatory processes, resulting in aberrant vascular proliferation and consequent visual impairment. Existing therapeutic interventions for CoNV demonstrate limited efficacy and potential for adverse reactions. Protein arginine methyltransferase 1 (PRMT1) is associated with the regulation of inflammation and M2 macrophage polarization. Nevertheless, the precise mechanism by which PRMT1 operates in CoNV remains uncertain. This study explored the impact of PRMT1 inhibition in a murine model of CoNV induced by alkali burn. Our findings indicated a direct relationship between PRMT1 levels and corneal damage. Moreover, our observations indicated an increase in fibroblast growth factor 2 (FGF2) expression in CoNV, which was reduced after treatment with a PRMT1 inhibitor. The inhibition of PRMT1 alleviated both corneal injury and CoNV, as evidenced by decreased corneal opacity and neovascularization. Immunofluorescence analysis and evaluation of inflammatory factor expression demonstrated that PRMT1 inhibition attenuated M2 macrophage polarization, a phenomenon that was reversed by the administration of recombinant FGF2 protein. These results were confirmed through experimentation on Human Umbilical Vein Endothelial Cells (HUVECs) and Mouse leukemia cells of monocyte macrophage cells (RAW264.7). Furthermore, it was established that FGF2 played a role in PI3K/Akt signal transduction, a critical regulatory pathway for M2 macrophage polarization. Importantly, the activity of this pathway was found to be suppressed by PRMT1 inhibitors. Mechanistically, PRMT1 was shown to promote M2 macrophage polarization, thereby contributing to CoNV, through the FGF2/PI3K/Akt pathway. Therefore, targeting PRMT1 may offer a promising therapeutic approach.

Correlation Between Anterior Chamber Angle Status and Limbal Stem Cell Deficiency in Primary Angle-Closure Glaucoma.

PURPOSE: To investigate the correlation between the opening and closing states of anterior chamber angle (ACA) and the density of limbal epithelial basal cells (LEBCs) in subjects with primary angle-closure glaucoma (PACG). DESIGN: Cross-sectional observational study. METHODS: A total of 54 eyes of 29 patients diagnosed with PACG were included in the study. Fifty-four eyes from normal subjects were included as control. Automatic evaluation system for ultrasound biomicroscopy images of anterior chamber angle was used to assist ophthalmologists in identifying the opening or closing state of ACA, and the in vivo confocal microscopy (IVCM) was used to evaluate the density of LEBCs in different directions. RESULTS: (1) The average density of LEBCs in the superior, inferior, nasal, and temporal limbus of the eyes in the PACG group was lower than that in the control group, and this pattern did not align with the density distribution observed in the control group. (2) In the early, moderate and advanced PACG, the density of LEBCs corresponding to the closed angle was lower than that in the control group (P < .05). Compared with the density of LEBCs corresponding to the closed angle and the open angle, the closed angle of PACG in the early, moderate and advanced stages was less than that in the open angle (P < .05 in the early and moderate stages; advanced stage P > .05). (3) The basal cell density was processed by dimensionless analysis. In the data calculated by averaging and minimizing, both closed angle dimensionless values were smaller than the open angle (P < .05). (4) Comparative analysis was conducted among the normal, open-angle, and closed-angle conditions in the superior, inferior, nasal, and temporal limbus. In the early stage of PACG, significant differences were observed in 4 limbal regions (P < .05), while in the moderate PACG stage, this difference was noted in 3 limbal regions (P < .05). In advanced PACG, 2 limbal regions exhibited significant differences (P < .05). These findings suggest that during the early PACG stage, angle closure is the predominant influencing factor on LEBCs density, while in the advanced stage, the decrease in density is attributed to a combination of angle closure and the natural progression of the disease. CONCLUSIONS: There is a significant correlation between anterior chamber angle status and LEBCs. Advanced PACG and angle closure should be highly suspected of the occurrence of limbal stem cell deficiency (LSCD).

Investigating the Causal Relationship Between Sleep Behaviors and Primary Open-Angle Glaucoma: A Bidirectional Two-Sample Mendelian Randomization Study.

Background: Although previous studies of sleep-related behaviors in relation to primary open-angle glaucoma (POAG) have been noted, the causal relationship remains unclear. The purpose of our present study was to investigate the relationships of genetically predicted sleep traits with POAG using a two-sample bidirectional Mendelian randomization (MR) method. Methods: Summary-level data collected from publicly available genome-wide association studies (GWAS) of European decent were applied for the bidirectional MR analysis. After quality control steps, independent single-nucleotide polymorphisms for eight sleep behaviors and POAG were selected as the genetic instruments. The inverse-variance weighted (IVW) approach was adopted as the primary method, which was complemented by a series of sensitivity analyses to assess the robustness of the results by estimating heterogeneity and pleiotropy. Multivariable MR (MVMR) was used to assess the direct effect of sleep traits on POAG, after adjusting for several confounding factors. Results: Our investigation revealed a positive correlation between genetically predicted ease of getting up in the morning and sleep duration and POAG using the IVW method (odds ratio (OR)=1.78, 95% confidence interval (CI):1.29-2.46, P = 4.33× 10-4; OR = 1.66, 95% CI:1.18-2.34, P = 3.38×10-3, respectively). Other supplementary MR methods also confirmed similar results. Moreover, the MVMR results also revealed that the adverse effects of these two sleep traits on POAG persisted after adjusting for body mass index, smoking, drinking, and education (all P < 0.05). Conversely, the relationships between genetic liability of POAG and different sleep behaviors were not statistically significant in the reverse-direction MR estimate (all P > 0.05). Conclusion: Our study demonstrated that genetic prediction of getting up easily in the morning or sleep duration were associated with a higher risk of POAG, but not vice versa, in a European population. Further validation and clinical interventions are required to offer potential strategies to prevent and manage POAG.

HMW-GSs 1Dx3+1Dy12 contribute to a suitable wheat gluten strength that confers superior Chinese steamed bread quality.

The aim of this study was to clarify the effects of the high-molecular-weight glutenin subunits (HMW-GSs) 1Dx3+1Dy12 (3+12) and 1Dx4+1Dy12 (4+12) at the Glu-D1 locus on gluten and Chinese steamed bread (CSB) quality. The grain protein content and composition, gluten content and gluten index, farinograph properties, and CSB quality were investigated using four wheat near-isogenic lines (NILs) carrying HMW-GSs 1Dx2+1Dy12 (2+12), 3+12, 4+12 and 1Dx5+1Dy10 (5+10), respectively. The unextractable polymeric protein (UPP) and glutenin macropolymer (GMP) content, gluten index, dough development time, stability time, and farinograph quality number of four NILs all ranked as 5+10 > 3+12 > 2+12/4+12, such as the gluten index ranked as 5+10(44.88%) > 3+12(40.07%) > 2+12(37.46%)/4+12(35.85%); however, their contributions to the quality of CSB were ranked as 3+12 > 5+10 > 2+12/4+12, such as the specific volume ranked as 3+12(2.64 mL/g) > 5+10(2.49 mL/g) > 2+12(2.36 mL/g)/4+12(2.35 mL/g), which indicated that a suitable gluten strength (3+12) was crucial to making high-quality CSB. In addition, subunits 4+12 had a similar quality performance to low-quality subunits 2+12. All these findings suggested that, except for the acknowledged high-quality subunits 5+10, the introduction of 3+12 at the Glu-D1 locus is an efficient way for quality improvement of gluten as well as CSB.

Identification of the immune-associated characteristics and predictive biomarkers of keratoconus based on single-cell RNA-sequencing and bulk RNA-sequencing.

Background: Whether keratoconus (KC) is an inflammatory disease is currently debated. Hence, we aimed to investigate the immune-related features of KC based on single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (bulk RNA-seq) data. Methods: scRNA-seq data were obtained from the Genome Sequence Archive (GSA), bulk RNA-seq data were obtained from the Gene Expression Omnibus (GEO), and immune-associated genes(IAGs) were obtained from the ImmPort database. Cell clusters of KC were annotated, and different cell clusters were then selected. The IAG score of each cell was calculated using the AUCell package. Three bulk RNA-seq datasets were merged and used to identify the differentially expressed genes (DEGs), biological functions, and immune characteristics. Weighted gene coexpression network analysis (WGCNA) was used to select the IAG score-related hub genes. Based on scRNA-seq and bulk RNA-seq analyses, three machine learning algorithms, including random forest (RF), support vector machine (SVM), and least absolute shrinkage and selection operator (LASSO) regression analysis, were used to identify potential prognostic markers for KC. A predictive nomogram was developed based on prognostic markers. Results: Six cell clusters were identified in KC, and decreased corneal stromal cell-5 (CSC-5) and increased CSC-6 were found in KC. CSC and immune cell clusters had the highest IAG scores. The bulk RNA-seq analysis identified 1362 DEGs (553 upregulated and 809 downregulated) in KC. We found different immune cell populations and differentially expressed cytokines in KC. More than three key IAG score-related modules and 367 genes were identified. By integrating the scRNA-seq and bulk RNA-seq analyses, 250 IAGs were selected and then incorporated into three machine learning models, and 10 IAGs (CEP112, FYN, IFITM1, IGFBP5, LPIN2, MAP1B, RNASE1, RUNX3, SMIM10, and SRGN) were identified as potential prognostic genes that were significantly associated with cytokine and matrix metalloproteinase(MMP)1-14 expression. Finally, a predictive nomogram was constructed and validated. Conclusion: Taken together, our results identified CSCs and immune cell clusters that may play a key role during KC progression by regulating immunological features and maintaining cell stability.

Deep Learning-Based Model for Automatic Assessment of Anterior Angle Chamber in Ultrasound Biomicroscopy.

OBJECTIVE: The goal of the work described here was to develop and assess a deep learning-based model that could automatically segment anterior chamber angle (ACA) tissues; classify iris curvature (I-Curv), iris root insertion (IRI), and angle closure (AC); automatically locate scleral spur; and measure ACA parameters in ultrasound biomicroscopy (UBM) images. METHODS: A total of 11,006 UBM images were obtained from 1538 patients with primary angle-closure glaucoma who were admitted to the Eye Center of Renmin Hospital of Wuhan University (Wuhan, China) to develop an imaging database. The UNet++ network was used to segment ACA tissues automatically. In addition, two support vector machine (SVM) algorithms were developed to classify I-Curv and AC, and a logistic regression (LR) algorithm was developed to classify IRI. Meanwhile, an algorithm was developed to automatically locate the scleral spur and measure ACA parameters. An external data set of 1,658 images from Huangshi Aier Eye Hospital was used to evaluate the performance of the model under different conditions. An additional 439 images were collected to compare the performance of the model with experts. RESULTS: The model achieved accuracies of 95.2%, 88.9% and 85.6% in classification of AC, I-Curv and IRI, respectively. Compared with ophthalmologists, the model achieved an accuracy of 0.765 in classifying AC, I-Curv and IRI, indicating that its high accuracy was as high as that of the ophthalmologists (p > 0.05). The average relative errors (AREs) of ACA parameters were smaller than 15% in the internal data sets. Intraclass correlation coefficients (ICCs) of all the angle-related parameters were greater than 0.911. ICC values of all iris thickness parameters were greater than 0.884. The accurate measurement of ACA parameters partly depended on accurate localization of the scleral spur (p < 0.001). CONCLUSION: The model could effectively and accurately evaluate the ACA automatically based on fully automated analysis of UBM images, and it can potentially be a promising tool to assist ophthalmologists. The present study suggested that the deep learning model can be extensively applied to the evaluation of ACA and AC-related biometric risk factors, and it may broaden the application of UBM imaging in the clinical research of primary angle-closure glaucoma.

Effects of Low-Molecular-Weight Glutenin Subunit Encoded by Glu-A3 on Gluten and Chinese Fresh Noodle Quality.

Low-molecular-weight glutenin subunits (LMW-GS) account for 40% of the total wheat grain gluten protein fraction, which plays a significant role in the formation of noodle processing quality. The goal of this study was to clarify the effects of the major LMW-GS encoded by Glu-A3 on gluten and Chinese fresh noodle (CFN) quality. Four near-isogenic lines (NILs) were used as materials in this study, respectively carrying alleles Glu-A3a, Glu-A3b, Glu-A3c, and Glu-A3e, against the background of wheat variety Xiaoyan 22. The grain protein and its component contents and the gluten content, gluten index, farinograph properties, cooking quality, and textural quality of CFN were investigated. The results show that the ratios of glutenin to gliadin (Glu/Gli) in the NILs ranked them as Glu-A3b > Glu-A3c/Glu-A3a > Glu-A3e, and the unextractable polymeric protein content (UPP%), gluten index (GI), and farinograph quality in the NILs ranked them as Glu-A3b > Glu-A3c > Glu-A3a/Glu-A3e. Compared to Glu-A3b and Glu-A3a, the NILs carrying alleles Glu-A3c and Glu-A3e had better cooking and texture properties in CFN. All these findings suggest that the introduction of alleles Glu-A3c or Glu-A3e is an efficient method for quality improvement in CFN, which provides an excellent subunit selection for improving CFN quality.

Evaluation of a computer-aided diagnostic model for corneal diseases by analyzing in vivo confocal microscopy images.

Objective: In order to automatically and rapidly recognize the layers of corneal images using in vivo confocal microscopy (IVCM) and classify them into normal and abnormal images, a computer-aided diagnostic model was developed and tested based on deep learning to reduce physicians' workload. Methods: A total of 19,612 corneal images were retrospectively collected from 423 patients who underwent IVCM between January 2021 and August 2022 from Renmin Hospital of Wuhan University (Wuhan, China) and Zhongnan Hospital of Wuhan University (Wuhan, China). Images were then reviewed and categorized by three corneal specialists before training and testing the models, including the layer recognition model (epithelium, bowman's membrane, stroma, and endothelium) and diagnostic model, to identify the layers of corneal images and distinguish normal images from abnormal images. Totally, 580 database-independent IVCM images were used in a human-machine competition to assess the speed and accuracy of image recognition by 4 ophthalmologists and artificial intelligence (AI). To evaluate the efficacy of the model, 8 trainees were employed to recognize these 580 images both with and without model assistance, and the results of the two evaluations were analyzed to explore the effects of model assistance. Results: The accuracy of the model reached 0.914, 0.957, 0.967, and 0.950 for the recognition of 4 layers of epithelium, bowman's membrane, stroma, and endothelium in the internal test dataset, respectively, and it was 0.961, 0.932, 0.945, and 0.959 for the recognition of normal/abnormal images at each layer, respectively. In the external test dataset, the accuracy of the recognition of corneal layers was 0.960, 0.965, 0.966, and 0.964, respectively, and the accuracy of normal/abnormal image recognition was 0.983, 0.972, 0.940, and 0.982, respectively. In the human-machine competition, the model achieved an accuracy of 0.929, which was similar to that of specialists and higher than that of senior physicians, and the recognition speed was 237 times faster than that of specialists. With model assistance, the accuracy of trainees increased from 0.712 to 0.886. Conclusion: A computer-aided diagnostic model was developed for IVCM images based on deep learning, which rapidly recognized the layers of corneal images and classified them as normal and abnormal. This model can increase the efficacy of clinical diagnosis and assist physicians in training and learning for clinical purposes.

A literature-derived dataset on risk factors for dry eye disease.

Dry eye disease (DED) is a common disease associated with disorder of tear secretion. Research on risk factors for DED, such as depression, arthritis, thyroid disease, stroke and diabetes, is important to facilitate its diagnosis and prognosis. We created a dataset on risk factors for DED (DrDED) with public access that can provide up-to-date and validated data acquired from systematically searched and screened, high-quality studies. The established DrDED contained 119 studies published between 2000 and 2022. The range of the study sample size was from 43 to 4,871,504. The study types were, as follows: cross-sectional (n = 92), retrospective cohort (n = 9), prospective cohort (n = 10), and case-control (n = 8) studies. Data from eligible studies were collected and presented for the present study, including the publication information, study characteristics, definition and prevalence of the disease, and risk factors for DED, together with the strength of association. With the publication of new relevant studies, the DrDED will be updated, and the data will be made accessible to the users. Design Type(s) Dataset creation objective Measurement Type(s) Patient outcome • scientific publication • risk factors • dry eye disease Technology Type(s) Digital curation • documenting • meta-analysis Factor Type(s) Depression • arthritis • thyroid disease • stroke disease • diabetes Sample Characteristic(s) Homo sapiens • dry eye disease • global.

Enantioseparation of amino acid and mandelic acid enantiomers using Josiphos-metal complexes as chiral extractants.

The development of new and efficient chiral extractants has always been the research hotspot and difficulty in the field of chiral extraction. Josiphos, a famous ferrocene derivative catalyst, is employed as a chiral extractant in enantioseparation of amino acid and mandelic acid enantiomers. The influences of metal ions, organic solvents, pH of the aqueous solution, extractant concentrations, and extraction temperature on enantioselectivities are systematically studied. The result reveals that Josiphos-Pd has good capabilities to enantioseparate 4-nitro-phenylalanine (Nphe), 3-chloro-phenylglycine (Cpheg), and mandelic acid (MA) with separation factors (α) of 3.30, 2.65, and 2.18, respectively. The pH of the aqueous phase and Josiphos-Pd concentration affect the extraction significantly, whereas extraction temperature shows little influence. After optimizing by response surface method, the mathematical models for extractions are established. And the highest experimental performance factors (pf) for Nphe, Cpheg, and MA are 0.1843, 0.1335, and 0.08884, respectively.

Genome-wide identification of foxtail millet's TRX family and a functional analysis of SiNRX1 in response to drought and salt stresses in transgenic Arabidopsis.

Thioredoxins (TRXs) are small-molecule proteins with redox activity that play very important roles in the growth, development, and stress resistance of plants. Foxtail millet (Setaria italica) gradually became a model crop for stress resistance research because of its advantages such as its resistance to sterility and its small genome. To date, the thioredoxin (TRX) family has been identified in Arabidopsis thaliana, rice and wheat. However, studies of the TRX family in foxtail millet have not been reported, and the biological function of this family remains unclear. In this study, 35 SiTRX genes were identified in the whole genome of foxtail millet through bioinformatic analysis. According to phylogenetic analysis, 35 SiTRXs can be divided into 13 types. The chromosome distribution, gene structure, cis-elements and conserved protein motifs of 35 SiTRXs were characterized. Three nucleoredoxin (NRX) members were further identified by a structural analysis of TRX family members. The expression patterns of foxtail millet's SiNRX members under abiotic stresses showed that they have different stress-response patterns. In addition, subcellular localization revealed that SiNRXs were localized to the nucleus, cytoplasm and membrane. Further studies demonstrated that the overexpression of SiNRX1 enhanced Arabidopsis' tolerance to drought and salt stresses, resulting in a higher survival rate and better growth performance. Moreover, the expression levels of several known stress-related genes were generally higher in overexpressed lines than in the wild-type. Thus, this study provides a general picture of the TRX family in foxtail millet and lay a foundation for further research on the mechanism of the action of TRX proteins on abiotic stresses.

Automatic identification of meibomian gland dysfunction with meibography images using deep learning.

BACKGROUND: Artificial intelligence is developing rapidly, bringing increasing numbers of intelligent products into daily life. However, it has little progress in dry eye, which is a common disease and associated with meibomian gland dysfunction (MGD). Noninvasive infrared meibography, known as an effective diagnostic tool of MGD, allows for objective observation of meibomian glands. Thus, we discuss a deep learning method to measure and assess meibomian glands of meibography. METHODS: We used Mask R-CNN deep learning (DL) framework. A total of 1878 meibography images were collected and manually annotated by two licensed eyelid specialists with two classes: conjunctiva and meibomian glands. The annotated pictures were used to establish a DL model. An independent test dataset that contained 58 images was used to compare the accuracy and efficiency of the deep learning model with specialists. RESULTS: The DL model calculated the ratio of meibomian gland loss with precise values by achieving high accuracy in the identification of conjunctiva (validation loss < 0.35, mAP > 0.976) and meibomian glands (validation loss < 1.0, mAP > 0.92). The comparison between specialists' annotation and the DL model evaluation showed that there is little difference between the gold standard and the model. Each image takes 480 ms for the model to evaluate, almost 21 times faster than specialists. CONCLUSIONS: The DL model can improve the accuracy of meibography image evaluation, help specialists to grade the meibomian glands and save their time to some extent.

Enantioseparation of amino acid and mandelic acid enantiomers using Garphos derivatives as chiral extractants.

In this paper, Garphos with different substituents were employed as chiral extractants to enantioseparate racemic amino acid and mandelic acid. The influences of metal precursors, pH of aqueous solution, Garphos-metal concentration, extraction temperature, and substituent effect on extraction were investigated. The results indicated that the substituent groups significantly affected the π-π interaction between extractant and substrate. And the separation factors (α) for Garphos could be remarkably improved by regulating substituent groups. Garphos-II-Pd, Garphos-VI-Pd, Garphos-III-Pd, Garphos-I-Cu, Garphos-VI-Cu, and Garphos-V-Pd were the most efficient extractants for phenylalanine (Phe), homophenylalanine (Hphe), 4-nitrophenylalanine (Nphe), 3-chlorophenylglycine (Cpheg), mandelic acid (MA), and 2-chlormandelic acid (CMA) with α values of 2.40, 2.37, 5.37, 1.59, 5.98, and 3.69, respectively. This work provided an important reference for the design of efficient chiral extractants in future work.

Nucleoredoxin Gene TaNRX1 Positively Regulates Drought Tolerance in Transgenic Wheat (Triticum aestivum L.).

Drought is the main abiotic stress factor limiting the growth and yield of wheat (Triticum aestivum L.). Therefore, improving wheat tolerance to drought stress is essential for maintaining yield. Previous studies have reported on the important role of TaNRX1 in conferring drought stress tolerance. Therefore, to elucidate the regulation mechanism by which TaNRX1 confers drought resistance in wheat, we generated TaNRX1 overexpression (OE) and RNA interference (RNAi) wheat lines. The results showed that the tolerance of the OE lines to drought stress were significantly enhanced. The survival rate, leaf chlorophyll, proline, soluble sugar content, and activities of the antioxidant enzymes (catalase, superoxide dismutase, and peroxidase) of the OE lines were higher than those of the wild type (WT); however, the relative electrical conductivity and malondialdehyde, hydrogen peroxide, and superoxide anion levels of the OE lines were lower than those of the WT; the RNAi lines showed the opposite results. RNA-seq results showed that the common differentially expressed genes of TaNRX1 OE and RNAi lines, before and after drought stress, were mainly distributed in the plant-pathogen interaction, plant hormone signal transduction, phenylpropane biosynthesis, starch and sucrose metabolism, and carbon metabolism pathways and were related to the transcription factors, including WRKY, MYB, and bHLH families. This study suggests that TaNRX1 positively regulates drought stress tolerance in wheat.

Construction and Validation of a Ferroptosis-Related Prognostic Model for Endometrial Cancer.

Endometrial cancer (EC) is one of the most common female reproductive system tumors, with close to 200,000 new cases each year. It accounts for approximately 7% of the total number of female cancers, but until now the cause of EC has remained unclear. Ferroptosis is regulated cell death that distinguishes apoptosis and caused by oxidative damage. The process has unique biological effects on metabolism and redox biology. In this study, we analyzed the relationship between EC and ferroptosis. According to the different expression levels of related genes, we first divided 544 EC samples into four clusters and found that most of the infiltrating immune cells were significantly different among the four groups. A differential gene expression analysis between Fe.cluster groups was performed, and the samples were again divided into three Fe.gene.cluster groups. The molecular characteristics and clinical characteristics of the groups were significantly different. Finally, 13 characteristic genes were selected as ferroptosis gene signatures, and the Fe.score was obtained by calculation. The Fe.score is closely related to the clinical and molecular characteristics of EC, and a low Fe.score has a significant survival advantage. The GDSC predicts that the IC50 of multiple chemotherapeutic drugs is also significantly different between the two groups. In conclusion, our research has explored the relationship between EC and ferroptosis in detail, provides comprehensive insights for ferroptosis-mediated EC mechanism research, and emphasizes the clinical application potential of Fe.score-based immunotherapy strategies.

miR-497 defect contributes to gastric cancer tumorigenesis and progression via regulating CDC42/ITGB1/FAK/PXN/AKT signaling.

Gastric cancer (GC) is one of the leading causes of cancer-related death worldwide. MicroRNAs (miRNAs) are known to be important regulators of GC. This study aims to investigate the role of miRNA (miR)-497 in GC. We demonstrated that the expression of miR-497 was downregulated in human GC tissues. After N-methyl-N-nitrosourea treatment, the incidence of GC in miR-497 knockout mice was significantly higher than that in wild-type mice. miR-497 overexpression suppressed GC cell proliferation, cell-cycle progression, colony formation, anti-apoptosis ability, and cell migration and invasion capacity. Additionally, miR-497 overexpression decreased the expression levels of cell division cycle 42 (CDC42) and integrin ß1 (ITGB1) and inhibited the phosphorylation of focal adhesion kinase (FAK), paxillin (PXN), and serine-threonine protein kinase (AKT). Furthermore, overexpression of miR-497 inhibited the metastasis of GC cells in vivo, which could be counteracted by CDC42 restoration. Furthermore, the focal adhesion of GC cells was found to be regulated by miR-497/CDC42 axis via ITGB1/FAK/PXN/AKT signaling. Collectively, it is concluded that miR-497 plays an important role in the repression of GC tumorigenesis and progression, partly via the CDC42/ITGB1/FAK/PXN/AKT pathway.

Exploring the Mechanism of the miRNA-145/Paxillin Axis in Cell Metabolism During VEGF-A-Induced Corneal Angiogenesis.

Purpose: Paxillin (PXN) is a key component of focal adhesions and plays an important role in angiogenesis. The aim of the present study was to investigate the effect of PXN in vascular endothelial growth factor A (VEGF-A)-induced angiogenesis in human umbilical vein endothelial cells (HUVECs). Methods: HUVECs were transfected with PXN overexpression and PXN interference vectors. Biochemical detection was used to detect adenosine triphosphate and lactic acid production. The morphology of mitochondria was observed under an electron microscope, and flow cytometry was conducted to measure mitochondrial membrane potential. Transwell experiments were used to detect the migration and tube formation ability of each group of cells. The expression of hexokinase (HK)1, HK2, glucose transporter 1 (GLUT1), phosphorylated phosphatidylinositol 3-kinase (PI3K), phosphorylated AKT, and phosphorylated mechanistic target of rapamycin (mTOR) was evaluated by western blot. Results: PXN silencing reduced the levels of lactic acid and adenosine triphosphate, downregulated HK1, HK2, and GLUT1, suppressed PI3K/AKT/mTOR signaling activation, and inhibited VEGF-A-induced mitochondria injury in VEGF-A-induced HUVECs. We also determined that miR-145-5p decreased the VEGF-A-induced expression of PXN and inhibited the invasion and angiogenesis of HUVECs. Also, miR-145-5p inhibition blocked the protective effect of PXN interference on VEGF-A-induced HUVEC injury. Furthermore, PXN interference significantly decreased lactic acid and adenosine triphosphate levels, inhibited PI3K/AKT/mTOR activation, and decreased the levels of HK1, HK2, and GLUT1 in VEGF-A-treated mouse corneal. Conclusions: The results indicate that PXN silencing inhibited the VEGF-A-induced invasion and angiogenesis of HUVECs via regulation of cell metabolism and mitochondrial damage, suggesting that PXN may be a potential target for antiangiogenic therapies.

A Gastrointestinal Endoscopy Quality Control System Incorporated With Deep Learning Improved Endoscopist Performance in a Pretest and Post-Test Trial.

INTRODUCTION: Gastrointestinal endoscopic quality is operator-dependent. To ensure the endoscopy quality, we constructed an endoscopic audit and feedback system named Endo.Adm and evaluated its effect in a form of pretest and posttest trial. METHODS: Endo.Adm system was developed using Python and Deep Convolutional Neural Ne2rk models. Sixteen endoscopists were recruited from Renmin Hospital of Wuhan University and were randomly assigned to undergo feedback of Endo.Adm or not (8 for the feedback group and 8 for the control group). The feedback group received weekly quality report cards which were automatically generated by Endo.Adm. We then compared the adenoma detection rate (ADR) and gastric precancerous conditions detection rate between baseline and postintervention phase for endoscopists in each group to evaluate the impact of Endo.Adm feedback. In total, 1,191 colonoscopies and 3,515 gastroscopies were included for analysis. RESULTS: ADR was increased after Endo.Adm feedback (10.8%-20.3%, P < 0.01,