Image1_Fundus photograph-based cataract evaluation network using deep learning.TIF

Background: Our study aims to develop an artificial intelligence-based high-precision cataract classification and grading evaluation network using fundus images.Methods: We utilized 1,340 color fundus photographs from 875 participants (aged 50–91 years at image capture) from the Beijing Eye Study 2011. Four experienced and trained ophthalmologists performed the classification of these cases based on slit-lamp and retro-illuminated images. Cataracts were classified into three types based on the location of the lens opacity: cortical cataract, nuclear cataract, and posterior subcapsular cataract. We developed a Dual-Stream Cataract Evaluation Network (DCEN) that uses color photographs of cataract fundus to achieve simultaneous cataract type classification and severity grading. The accuracy of severity grading was enhanced by incorporating the results of type classification.Results: The DCEN method achieved an accuracy of 0.9762, a sensitivity of 0.9820, an F1 score of 0.9401, and a kappa coefficient of 0.8618 in the cataract classification task. By incorporating type features, the grading of cataract severity can be improved with an accuracy of 0.9703, a sensitivity of 0.9344, an F1 score of 0.9555, and a kappa coefficient of 0.9111. We utilized Grad-CAM visualization technology to analyze and summarize the fundus image features of different types of cataracts, and we verified our conclusions by examining the information entropy of the retinal vascular region.Conclusion: The proposed DCEN provides a reliable ability to comprehensively evaluate the condition of cataracts from fundus images. Applying deep learning to clinical cataract assessment has the advantages of simplicity, speed, and efficiency.</p

Image2_Fundus photograph-based cataract evaluation network using deep learning.PNG

Background: Our study aims to develop an artificial intelligence-based high-precision cataract classification and grading evaluation network using fundus images.Methods: We utilized 1,340 color fundus photographs from 875 participants (aged 50–91 years at image capture) from the Beijing Eye Study 2011. Four experienced and trained ophthalmologists performed the classification of these cases based on slit-lamp and retro-illuminated images. Cataracts were classified into three types based on the location of the lens opacity: cortical cataract, nuclear cataract, and posterior subcapsular cataract. We developed a Dual-Stream Cataract Evaluation Network (DCEN) that uses color photographs of cataract fundus to achieve simultaneous cataract type classification and severity grading. The accuracy of severity grading was enhanced by incorporating the results of type classification.Results: The DCEN method achieved an accuracy of 0.9762, a sensitivity of 0.9820, an F1 score of 0.9401, and a kappa coefficient of 0.8618 in the cataract classification task. By incorporating type features, the grading of cataract severity can be improved with an accuracy of 0.9703, a sensitivity of 0.9344, an F1 score of 0.9555, and a kappa coefficient of 0.9111. We utilized Grad-CAM visualization technology to analyze and summarize the fundus image features of different types of cataracts, and we verified our conclusions by examining the information entropy of the retinal vascular region.Conclusion: The proposed DCEN provides a reliable ability to comprehensively evaluate the condition of cataracts from fundus images. Applying deep learning to clinical cataract assessment has the advantages of simplicity, speed, and efficiency.</p

Image3_Fundus photograph-based cataract evaluation network using deep learning.PNG

Background: Our study aims to develop an artificial intelligence-based high-precision cataract classification and grading evaluation network using fundus images.Methods: We utilized 1,340 color fundus photographs from 875 participants (aged 50–91 years at image capture) from the Beijing Eye Study 2011. Four experienced and trained ophthalmologists performed the classification of these cases based on slit-lamp and retro-illuminated images. Cataracts were classified into three types based on the location of the lens opacity: cortical cataract, nuclear cataract, and posterior subcapsular cataract. We developed a Dual-Stream Cataract Evaluation Network (DCEN) that uses color photographs of cataract fundus to achieve simultaneous cataract type classification and severity grading. The accuracy of severity grading was enhanced by incorporating the results of type classification.Results: The DCEN method achieved an accuracy of 0.9762, a sensitivity of 0.9820, an F1 score of 0.9401, and a kappa coefficient of 0.8618 in the cataract classification task. By incorporating type features, the grading of cataract severity can be improved with an accuracy of 0.9703, a sensitivity of 0.9344, an F1 score of 0.9555, and a kappa coefficient of 0.9111. We utilized Grad-CAM visualization technology to analyze and summarize the fundus image features of different types of cataracts, and we verified our conclusions by examining the information entropy of the retinal vascular region.Conclusion: The proposed DCEN provides a reliable ability to comprehensively evaluate the condition of cataracts from fundus images. Applying deep learning to clinical cataract assessment has the advantages of simplicity, speed, and efficiency.</p

Solution NMR of a 463-Residue Phosphohexomutase: Domain 4 Mobility, Substates, and Phosphoryl Transfer Defect

Phosphomannomutase/phosphoglucomutase contributes to the infectivity of <i>Pseudomonas aeruginosa</i>, retains and reorients its intermediate by 180°, and rotates domain 4 to close the deep catalytic cleft. Nuclear magnetic resonance (NMR) spectra of the backbone of wild-type and S108C-inactivated enzymes were assigned to at least 90%. ¹³C secondary chemical shifts report excellent agreement of solution and crystallographic structure over the 14 α-helices, C-capping motifs, and 20 of the 22 β-strands. Major and minor NMR peaks implicate substates affecting 28% of assigned residues. These can be attributed to the phosphorylation state and possibly to conformational interconversions. The S108C substitution of the phosphoryl donor and acceptor slowed transformation of the glucose 1-phosphate substrate by impairing <i>k</i>_cat. Addition of the glucose 1,6-bisphosphate intermediate accelerated this reaction by 2–3 orders of magnitude, somewhat bypassing the defect and apparently relieving substrate inhibition. The S108C mutation perturbs the NMR spectra and electron density map around the catalytic cleft while preserving the secondary structure in solution. Diminished peak heights and faster ¹⁵N relaxation suggest line broadening and millisecond fluctuations within four loops that can contact phosphosugars. ¹⁵N NMR relaxation and peak heights suggest that domain 4 reorients slightly faster in solution than domains 1–3, and with a different principal axis of diffusion. This adds to the crystallographic evidence of domain 4 rotations in the enzyme, which were previously suggested to couple to reorientation of the intermediate, substrate binding, and product release

Solution NMR of a 463-Residue Phosphohexomutase: Domain 4 Mobility, Substates, and Phosphoryl Transfer Defect

Phosphomannomutase/phosphoglucomutase contributes to the infectivity of <i>Pseudomonas aeruginosa</i>, retains and reorients its intermediate by 180°, and rotates domain 4 to close the deep catalytic cleft. Nuclear magnetic resonance (NMR) spectra of the backbone of wild-type and S108C-inactivated enzymes were assigned to at least 90%. ¹³C secondary chemical shifts report excellent agreement of solution and crystallographic structure over the 14 α-helices, C-capping motifs, and 20 of the 22 β-strands. Major and minor NMR peaks implicate substates affecting 28% of assigned residues. These can be attributed to the phosphorylation state and possibly to conformational interconversions. The S108C substitution of the phosphoryl donor and acceptor slowed transformation of the glucose 1-phosphate substrate by impairing <i>k</i>_cat. Addition of the glucose 1,6-bisphosphate intermediate accelerated this reaction by 2–3 orders of magnitude, somewhat bypassing the defect and apparently relieving substrate inhibition. The S108C mutation perturbs the NMR spectra and electron density map around the catalytic cleft while preserving the secondary structure in solution. Diminished peak heights and faster ¹⁵N relaxation suggest line broadening and millisecond fluctuations within four loops that can contact phosphosugars. ¹⁵N NMR relaxation and peak heights suggest that domain 4 reorients slightly faster in solution than domains 1–3, and with a different principal axis of diffusion. This adds to the crystallographic evidence of domain 4 rotations in the enzyme, which were previously suggested to couple to reorientation of the intermediate, substrate binding, and product release

DataSheet1_AI-boosted CRISPR-Cas13a and total internal reflection fluorescence microscopy system for SARS-CoV-2 detection.docx

Integrating artificial intelligence with SARS-CoV-2 diagnostics can help in the timely execution of pandemic control and monitoring plans. To improve the efficiency of the diagnostic process, this study aims to classify fluorescent images via traditional machine learning and deep learning-based transfer learning. A previous study reported a CRISPR-Cas13a system combined with total internal reflection fluorescence microscopy (TIRFM) to detect the existence and concentrations of SARS-CoV-2 by fluorescent images. However, the lack of professional software and excessive manual labor hinder the practicability of the system. Here, we construct a fluorescent image dataset and develop an AI-boosted CRISPR-Cas13a and total internal reflection fluorescence microscopy system for the rapid diagnosis of SARS-CoV-2. Our study proposes Fluorescent Images Classification Transfer learning based on DenseNet-121 (FICTransDense), an approach that uses TIRF images (before and after sample introduction, respectively) for preprocessing, including outlier exclusion and setting and division preprocessing (i.e., SDP). Classification results indicate that the FICTransDense and Decision Tree algorithms outperform other approaches on the SDP dataset. Most of the algorithms benefit from the proposed SDP technique in terms of Accuracy, Recall, F1 Score, and Precision. The use of AI-boosted CRISPR-Cas13a and TIRFM systems facilitates rapid monitoring and diagnosis of SARS-CoV-2.</p