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Ophthalmology is a subject that highly depends on imaging examination. Artificial intelligence (AI) technology has great potential in medical imaging analysis, including image diagnosis, classification, grading, guiding treatment and evaluating prognosis. The combination of the two can realize mass screening of grass-roots eye health, making it possible to seek medical treatment in the mode of “first treatment at the grass-roots level, two-way referral, emergency and slow treatment, and linkage between the upper and lower levels”. On the basis of summarizing the AI technology carried out by scholars and their teams all over the world in the field of ophthalmology, quite a lot of studies have confirmed that machine learning can assist in diagnosis, grading, providing optimal treatment plans and evaluating prognosis in corneal and conjunctival diseases, ametropia, lens diseases, glaucoma, iris diseases, etc. This paper systematically shows the application and progress of AI technology in common anterior segment ocular diseases, the current limitations, and prospects for the future.

Abstract Neuroligin (NLGN) 3 is a postsynaptic cell adhesion protein organizing synapse formation through two different types of transsynaptic interactions, canonical interaction with neurexins (NRXNs) and a recently identified noncanonical interaction with protein tyrosine phosphatase (PTP) δ. Although, NLGN3 gene is known as a risk gene for neurodevelopmental disorders such as autism spectrum disorder (ASD) and intellectual disability (ID), the pathogenic contribution of the canonical NLGN3-NRXN and noncanonical NLGN3-PTPδ pathways to these disorders remains elusive. In this study, we utilized Nlgn3 mutant mice selectively lacking the interaction with either NRXNs or PTPδ and investigated their social and memory performance. Neither Nlgn3 mutants showed any social cognitive deficiency in the social novelty recognition test. However, the Nlgn3 mutant mice lacking the PTPδ pathway exhibited significant decline in the social conditioned place preference (sCPP) at the juvenile stage, suggesting the involvement of the NLGN3-PTPδ pathway in the regulation of social motivation and reward. In terms of learning and memory, disrupting the canonical NRXN pathway attenuated contextual fear conditioning while disrupting the noncanonical NLGN3-PTPδ pathway enhanced it. Furthermore, disruption of the NLGN3-PTPδ pathway negatively affected the remote spatial reference memory in the Barnes maze test. These findings highlight the differential contributions of the canonical NLGN3-NRXN and noncanonical NLGN3-PTPδ synaptogenic pathways to the regulation of higher order brain functions associated with ASD and ID.

Abstract Background Restenosis after percutaneous coronary intervention (PCI) limits therapeutic revascularization. Neuropeptide Y (NPY), co-stored and co-released with the sympathetic nervous system, is involved in this process, but its exact role and underlying mechanisms remain to be fully understood. This study aimed to investigate the role of NPY in neointima formation after vascular injury. Methods Using the left carotid arteries of wild-type (WT, NPY-intact) and NPY-deficient (NPY−/−) mice, ferric chloride-mediated carotid artery injury induced neointima formation. Three weeks after injury, the left injured carotid artery and contralateral uninjured carotid artery were collected for histological analysis and immunohistochemical staining. RT-qPCR was used to detect the mRNA expression of several key inflammatory markers and cell adhesion molecules in vascular samples. Raw264.7 cells were treated with NPY, lipopolysaccharide (LPS), and lipopolysaccharide-free, respectively, and RT-qPCR was used to detect the expression of these inflammatory mediators. Results Compared with WT mice, NPY−/− mice had significantly reduced neointimal formation three weeks after injury. Mechanistically, immunohistochemical analysis showed there were fewer macrophages and more vascular smooth muscle cells in the neointima of NPY−/− mice. Moreover, the mRNA expression of key inflammatory markers such as interleukin-6 (IL-6), transforming growth factor-β1 (TGF-β1), and intercellular adhesion molecule-1 (ICAM-1) was significantly lower in the injured carotid arteries of NPY−/− mice, compared to that in the injured carotid arteries of WT mice. In RAW264.7 macrophages, NPY significantly promoted TGF-β1 mRNA expression under unactivated but not LPS-stimulated condition. Conclusions Deletion of NPY attenuated neointima formation after artery injury, at least partly, through reducing the local inflammatory response, suggesting that NPY pathway may provide new insights into the mechanism of restenosis.

Background Recent studies suggest machine learning represents a promising predictive option for patients in intensive care units (ICU). However, the machine learning performance regarding its actual predictive value for early detection in acute kidney injury (AKI) patients remains uncertain. Objective This study represents the inaugural meta-analysis aiming to investigate the predictive value of machine learning for assessing the risk of AKI among ICU patients. Methods PubMed, Web of Science, Embase, and the Cochrane Library were all thoroughly searched from inception to June 25, 2022. Eligible studies for inclusion were those concentrating on the predictive value and the development, validation, or enhancement of a prediction model for AKI patients in the ICU. Measures of effects, including c-index, sensitivity, specificity, and their corresponding 95% confidence intervals (CIs), were employed for analysis. The risk of bias in the included original studies was assessed using Probst. The meta-analysis in our study was carried out using R version 4.2.0. Results The systematic search yielded 29 articles describing 13 machine-learning models, including 86 models in the training set and 57 in the validation set. The overall c-index was 0.767 (95% CI [0.746, 0.788]) in the training set and 0.773 (95% CI [0.741, 0.804]) in the validation set. The sensitivity and specificity of included studies are as follows: sensitivity [train: 0.66 (95% CI [0.59, 0.73]), validation: 0.73 (95% CI [0.68, 0.77])]; and specificity [train: 0.83 (95% CI [0.78, 0.87])], validation: 0.75 (95% CI [0.71, 0.79])]. Conclusion The machine learning-based method for predicting the risk of AKI in hospital ICU patients has excellent predictive value and could potentially serve as a prospective application strategy for early identification. PROSPERO Registration number ID: CRD42022362838.

Background and Objectives: Endocan, secreted from the activated endothelium, is a key player in inflammation, endothelial dysfunction, proliferation of vascular smooth muscle cells, and angiogenesis. We aimed to investigate the link between endocan and aortic stiffness in maintenance hemodialysis (HD) patients. Materials and Methods: After recruiting HD patients from a medical center, their baseline characteristics, blood sample, and anthropometry were assessed and recorded. The serum endocan level was determined using an enzyme immunoassay kit, and carotid–femoral pulse wave velocity (cfPWV) measurement was used to evaluate aortic stiffness. Results: A total of 122 HD patients were enrolled. Aortic stiffness was diagnosed in 53 patients (43.4%), who were found to be older (p = 0.007) and have a higher prevalence of diabetes (p < 0.001) and hypertension (p = 0.030), higher systolic blood pressure (p = 0.011), and higher endocan levels (p < 0.001), when compared with their counterparts. On the multivariate logistic regression model, the development of aortic stiffness in patients on chronic HD was found to be associated with endocan [odds ratio (OR): 1.566, 95% confidence interval (CI): 1.224–2.002, p < 0.001], age (OR: 1.040, 95% CI: 1.001–1.080, p = 0.045), and diabetes (OR: 4.067, 95% CI: 1.532–10.798, p = 0.005), after proper adjustment for confounders (adopting diabetes, hypertension, age, systolic blood pressure, and endocan). The area under the receiver operating characteristic curve was 0.713 (95% CI: 0.620–0.806, p < 0.001) for predicting aortic stiffness by the serum endocan level, at an optimal cutoff value of 2.68 ng/mL (64.15% sensitivity, 69.57% specificity). Upon multivariate linear regression analysis, logarithmically transformed endocan was proven as an independent predictor of cfPWV (β = 0.405, adjusted R2 change = 0.152; p < 0.001). Conclusions: The serum endocan level positively correlated with cfPWV and was an independent predictor of aortic stiffness in chronic HD patients. [ABSTRACT FROM AUTHOR]

It is proposed a high resolution remote sensing image segmentation method which combines static minimum spanning tree (MST) tessellation considering shape information and the RHMRF-FCM algorithm. It solves the problems in the traditional pixel-based HMRF-FCM algorithm in which poor noise resistance and low precision segmentation in a complex boundary exist. By using the MST model and shape information, the object boundary and geometrical noise can be expressed and reduced respectively. Firstly, the static MST tessellation is employed for dividing the image domain into some sub-regions corresponding to the components of homogeneous regions needed to be segmented. Secondly, based on the tessellation results, the RHMRF model is built, and regulation terms considering the KL information and the information entropy are introduced into the FCM objective function. Finally, the partial differential method and Lagrange function are employed to calculate the parameters of the fuzzy objective function for obtaining the global optimal segmentation results. To verify the robustness and effectiveness of the proposed algorithm, the experiments are carried out with WorldView-3 (WV-3) high resolution image. The results from proposed method with different parameters and comparing methods (multi-resolution method and watershed segmentation method in eCognition software) are analyzed qualitatively and quantitatively.

Praying mantises are distributed all over the world. Though some Mantodea mitogenomes have been reported, an evolutionary genomic and phylogenetic analysis study lacks the latest taxonomic system. In the present study, four new mitogenomes were sequenced and annotated. Deroplatys truncate, D. lobate, Amorphoscelis chinensis and Macromantis sp. belong to Deroplatyidae, Amorphoscelidae and Photinaidae family, respectively. Our results indicated that the ATP8 gene may be lost in D. truncate and D. lobata mt genome, and four tRNA genes have not been found in D. truncate, D. lobata and Macromantis sp. A dN/dS pair analysis was conducted and it was found that all genes have evolved under purifying selection. Furthermore, we tested the phylogenetic relationships between the eight families of the Mantodea, including 35 species of praying Mantis. Based on the complete mitochondrial genome data, it was also suggested as sister to Deroplatyidae + Mantidae, Metallyticus sp., the only representative of Metallyticidae, is sister to the remaining mantises. Our results support the taxonomic system of Schwarz and Roy and are consistent with previous studies.