Accurate detection and grading of pterygium through smartphone by a fusion training model.

BACKGROUND/AIMS: To improve the accuracy of pterygium screening and detection through smartphones, we established a fusion training model by blending a large number of slit-lamp image data with a small proportion of smartphone data. METHOD: Two datasets were used, a slit-lamp image dataset containing 20 987 images and a smartphone-based image dataset containing 1094 images. The RFRC (Faster RCNN based on ResNet101) model for the detection model. The SRU-Net (U-Net based on SE-ResNeXt50) for the segmentation models. The open-cv algorithm measured the width, length and area of pterygium in the cornea. RESULTS: The detection model (trained by slit-lamp images) obtained the mean accuracy of 95.24%. The fusion segmentation model (trained by smartphone and slit-lamp images) achieved a microaverage F1 score of 0.8981, sensitivity of 0.8709, specificity of 0.9668 and area under the curve (AUC) of 0.9295. Compared with the same group of patients' smartphone and slit-lamp images, the fusion model performance in smartphone-based images (F1 score of 0.9313, sensitivity of 0.9360, specificity of 0.9613, AUC of 0.9426, accuracy of 92.38%) is close to the model (trained by slit-lamp images) in slit-lamp images (F1 score of 0.9448, sensitivity of 0.9165, specificity of 0.9689, AUC of 0.9569 and accuracy of 94.29%). CONCLUSION: Our fusion model method got high pterygium detection and grading accuracy in insufficient smartphone data, and its performance is comparable to experienced ophthalmologists and works well in different smartphone brands.

Altered functional brain networks in coronary heart disease: independent component analysis and graph theoretical analysis.

Coronary heart disease (CHD) confers a high risk of cognitive and mental impairments in patients. This study aimed to explore the association of CHD with functional connectivity and topological properties of brain networks. A total of 27 patients with CHD and 44 healthy controls (HCs) participated in this study and underwent a resting-state functional magnetic resonance imaging (rs-fMRI) scan. Intra- and internetwork functional connectivity alterations were explored using independent component analysis in CHD patients. Furthermore, graph theoretical analysis was adopted to assess abnormalities in small-world properties and network efficiency metrics of brain networks. Compared to HCs, CHD patients exhibited increased functional connectivity between the posterior default mode network and posterior visual network, as well as decreased functional connectivity between the left frontoparietal network and auditory network. In terms of graph theoretical analysis, small-world network topology was identified in both CHD patients and HCs. Furthermore, the nodal local efficiency of the left putamen was significantly decreased in CHD patients compared to HCs. This study revealed alterations in brain functional connectivity and topological properties in CHD patients, shedding light on the potential neurological mechanism underlying cognitive and mental impairments in these patients and suggesting unexplored connections between CHD and higher order cognitive processing.

Berberine ameliorate inflammation and apoptosis via modulating PI3K/AKT/NFκB and MAPK pathway on dry eye.

BACKGROUND: Dry eye disease (DED) is a multifactorial disease in ocular surface, and inflammation plays an etiological role. Berberine (BBR) has shown efficacy in treating inflammatory diseases. Yet, there was no adequate information related to the therapeutic effects of BBR for DED. PURPOSE: To detect the effects and explore the potential mechanisms of BBR on DED. STUDY DESIGN: In vitro, in vivo study and network pharmacology analysis were involved. METHOD: The human corneal epithelium cells viability was evaluated with different concentrations of BBR. Dry eye murine model was established by exposing to the desiccating stress, and Ciclosporin (CSA), BBR eye drops or vehicle were topical administration for 7 days. The phenol red cotton tests, Oregon-green-dextran staining and Periodic acid-Schiff staining were performed and evaluated the dry eye after treatment. Inflammation and apoptosis levels of ocular surface were quantified. The potential targets related to berberine and dry eye were collected from databases. The Protein-Protein interaction network analysis and GO & KEGG enrichment analysis were realized by STRING database, Metascape platform and Cytoscape software to find core targets and signaling pathways. The SchrÖdinger software was used to molecular docking and PyMOL software to visualization. Finally, the levels of PI3K/AKT/NFκB and MAPK pathways were detected. RESULT: The data revealed BBR could rescue impaired HCE under hyperosmotic conditions. In addition, BBR eye drops could ameliorate dry eye. And BBR eye drops suppressed the inflammatory factors and CD4+T cells infiltration in conjunctiva. Besides, BBR eye drops protected ocular surface by avoiding the severe apoptosis and decreasing the level of MMP-3 and MMP-9. 148 common targets intersection between BBR and dry eye were found via network pharmacology analysis. Core proteins and core pathways were identified through PPI and GO&KEGG enrichment analysis. Molecular docking displayed excellent binding between BBR and those core targets. Finally, in vivo study verified that BBR eye drops had a therapeutic effect in dry eye by inhibiting PI3K/AKT/NFκB and MAPK pathways. CONCLUSION: The research provided convincing evidence that BBR could be a candidate drug for dry eye.

Selective conversion of CO2 to isobutane-enriched C4 alkanes over InZrOx-Beta composite catalyst.

Direct conversion of CO2 to a single specific hydrocarbon with high selectivity is extremely attractive but very challenging. Herein, by employing an InZrOx-Beta composite catalyst in the CO2 hydrogenation, a high selectivity of 53.4% to butane is achieved in hydrocarbons (CO free) under 315 °C and 3.0 MPa, at a CO2 conversion of 20.4%. Various characterizations and DFT calculation reveal that the generation of methanol-related intermediates by CO2 hydrogenation is closely related to the surface oxygen vacancies of InZrOx, which can be tuned through modulating the preparation methods. In contrast, the three-dimensional 12-ring channels of H-Beta conduces to forming higher methylbenzenes and methylnaphthalenes containing isopropyl side-chain, which favors the transformation of methanol-related intermediates to butane through alkyl side-chain elimination and subsequent methylation and hydrogenation. Moreover, the catalytic stability of InZrOx-Beta in the CO2 hydrogenation is considerably improved by a surface silica protection strategy which can effectively inhibit the indium migration.

AES-CSFS: an automatic evaluation system for corneal sodium fluorescein staining based on deep learning.

Background: Corneal fluorescein sodium staining is a valuable diagnostic method for various ocular surface diseases. However, the examination results are highly dependent on the subjective experience of ophthalmologists. Objectives: To develop an artificial intelligence system based on deep learning to provide an accurate quantitative assessment of sodium fluorescein staining score and the size of cornea epithelial patchy defect. Design: A prospective study. Methods: We proposed an artificial intelligence system for automatically evaluating corneal staining scores and accurately measuring patchy corneal epithelial defects based on corneal fluorescein sodium staining images. The design incorporates two segmentation models and a classification model to forecast and assess the stained images. Meanwhile, we compare the evaluation findings from the system with ophthalmologists with varying expertise. Results: For the segmentation task of cornea boundary and cornea epithelial patchy defect area, our proposed method can achieve the performance of dice similarity coefficient (DSC) is 0.98/0.97 and Hausdorff distance (HD) is 3.60/8.39, respectively, when compared with the manually labeled gold standard. This method significantly outperforms the four leading algorithms (Unet, Unet++, Swin-Unet, and TransUnet). For the classification task, our algorithm achieves the best performance in accuracy, recall, and F1-score, which are 91.2%, 78.6%, and 79.2%, respectively. The performance of our developed system exceeds seven different approaches (Inception, ShuffleNet, Xception, EfficientNet_B7, DenseNet, ResNet, and VIT) in classification tasks. In addition, three ophthalmologists were selected to rate corneal staining images. The results showed that the performance of our artificial intelligence system significantly outperformed the junior doctors. Conclusion: The system offers a promising automated assessment method for corneal fluorescein staining, decreasing incorrect evaluations caused by ophthalmologists' subjective variance and limited knowledge.

[Analysis of 12 cases of fetal malformation in forensic medical malpractice].

OBJECTIVE: To explore the main reasons of medical malpractice of fetal abnormalities and to analyze the key points and the ideas in judicial appraisal. METHODS: According to the related laws, regulations and clinical practice guidelines, the medical faults and the contribution degree in 12 medical malpractice cases about fetal abnormalities were analyzed. RESULTS: There were medical faults in five cases. Among them, the doctors did not fulfill the duty of inform in 2 cases, did not analyze the abnormal results comprehensively in 2 cases, did not have qualified medical conditions and normative reports in 1 case. The hospitals needed to take the minor responsibility in 2 cases and slight responsibility in 2 cases. CONCLUSION: The key points in the judicial appraisal are whether the doctors abide by the related laws, regulations and clinical practice guidelines in prenatal examination, screen and diagnosis, and whether the doctors realize the limitations of ultrasonic testing and fulfill the obligation of inform.