Evaluation of choroid vascular layer thickness in wet age-related macular degeneration using artificial intelligence.

PURPOSE: To facilitate the assessment of choroid vascular layer thickness in patients with wet age-related macular degeneration (AMD) using artificial intelligence (AI). METHODS: We included 194 patients with wet AMD and 225 healthy participants. Choroid images were obtained using swept-source optical coherence tomography. The average Sattler layer-choriocapillaris complex thickness (SLCCT), Haller layer thickness (HLT), and choroidal thickness (CT) were auto-measured at 7 regions centered around the foveola using AI and subsequently compared between the 2 groups. RESULTS: The SLCCT was lower in the AMD group than in the control group (P < 0.05). The HLT was significantly higher in the AMD group than in the control group at the Tparafovea and T-perifovea in the total population (P < 0.05) and in the ≤70-year subgroup (P < 0.05). The CT was higher in the AMD group than in the control group, particularly at the N-perifovea, T-perifovea, and T-parafovea in the ≤70-year subgroup; Interestingly, it was lower in the AMD group than in the control group at the Nparafovea, N-fovea, foveola, and T-fovea in the >70-year subgroup (P < 0.05). CONCLUSION: This novel AI-based auto-measurement was more accurate, efficient, and detailed than manual measurements. SLCCT thinning was observed in wet AMD; however, CT changes depended on the interaction between HLT compensatory thickening and SLCCT thinning.

Comparison of Changes in Retinal Vascular Parameters and Density in Patients with Moyamoya Disease: A Retrospective Study.

INTRODUCTION: This study aimed to compare retinal vascular parameters and density in patients with moyamoya disease using the optical coherence tomography angiography. METHODS: This clinical trial totally enrolls 78 eyes from 39 participants, and all these patients with moyamoya disease (N = 13) are set as experimental group and participants with health who matched with age and gender are considered as the control group (N = 26). Then all these participants receive optical coherence tomography angiography detection. Participants' general data are collected and analyzed. Skeleton density (SD) value, vessel density (VD) value, fractal dimension (FD) value, vessel diameter index (VDI) value, foveal avascular zone (FAZ) value are analyzed. RESULTS: A total of 39 participants are included in this study. The SD value in the experimental group was significantly lower than that in control group (0.175 [0.166, 0.181] vs. 0.184 [0.175, 0.188], p = 0.017). Similarly, the VD value in the experimental group was significantly lower than that in the control group (0.333 [0.320, 0.350] vs. 0.354 [0.337, 0.364], p = 0.024). Additionally, the FD value in the experimental group was significantly lower than that in the control group (2.088 [2.083, 2.094] vs. 2.096 [2.090, 2.101], p = 0.022). As for the VDI and FAZ, VDI and FAZ values in the experimental group were lower than those in the control group, there was no significant difference in VDI and FAZ values between the two groups. CONCLUSIONS: Our study, using non-invasive and rapid OCTA imaging, confirmed decreased retinal vascular parameters and density in patients with moyamoya disease.

PD-1+CD8+ T Cells Proximal to PD-L1+CD68+ Macrophages Are Associated with Poor Prognosis in Pancreatic Ductal Adenocarcinoma Patients.

Immune complexity status in the TME has been linked to clinical outcomes in pancreatic ductal adenocarcinoma (PDAC) patients. TME assessments with current cell marker and cell density-based analyses do not identify the original phenotypes of single cells with multilineage selectivity, the functional status of the cells, or cellular spatial information in the tissues. Here, we describe a method that circumvents these problems. The combined strategy of multiplexed IHC with computational image cytometry and multiparameter cytometric quantification allows us to assess multiple lineage-selective and functional phenotypic biomarkers in the TME. Our study revealed that the percentage of CD8+ T lymphoid cells expressing the T cell exhaustion marker PD-1 and the high expression of the checkpoint PD-L1 in CD68+ cells are associated with a poor prognosis. The prognostic value of this combined approach is more significant than that of lymphoid and myeloid cell density analyses. In addition, a spatial analysis revealed a correlation between the abundance of PD-L1+CD68+ tumor-associated macrophages and PD-1+CD8+T cell infiltration, indicating pro-tumor immunity associated with a poor prognosis. These data highlight the implications of practical monitoring for understanding the complexity of immune cells in situ. Digital imaging and multiparameter cytometric processing of cell phenotypes in the TME and tissue architecture can reveal biomarkers and assessment parameters for patient stratification.

Spatio-Temporal Variation and Influencing Factors of the Coupling Coordination Degree of Production-Living-Ecological Space in China.

Territorial space is a multi-functional complex. The coordinated production-living-ecological space (PLES) effectively coordinates the man-land relationship, promotes regional sustainable development, and maximizes territorial space. How to build a high-quality national spatial layout and support system for development has become a hot topic of concern in all sectors of society. However, few studies have explored the coupling coordination considering the various production-living-ecological functions of land use type and its influencing factors of PLES at the county scale in China. To address the gap, based on the connotation of PLES theory, this study established a classification and evaluation system for PLES and analyzed the spatio-temporal characteristics, coupling coordination degree, spatial autocorrelation, and influencing factors of PLES in China from 2000 to 2020. The results are as follows: (1) The production space index and living space index in China showed a continuous increase tendency, while the ecological space index decreased continuously during the study period. The production space and living space were concentrated in the east of Hu Line, and the ecological space indexes in mountainous areas were significantly higher than those in plain areas during the study period. (2) The gravity centers of PLES all migrated to the west of China to different degrees during the study period. (3) From 2000 to 2020, the basically balanced category was the main coupling coordination type, and the number of seriously unbalanced categories accounted for the least. In the west of the Hu Line, the seriously unbalanced category was dominant, while in the east of the Hu Line were the moderately unbalanced categories and above. (4) During the study period, the low-low type was the main relationship type, widely distributed in western China, followed by the high-high type, mainly situated in the North China Plain, Yangtze River Delta, Pearl River Delta, Jianghan Plain, Chengdu Plain, Northeast China Plain, and some provincial capital cities. (5) Regression results showed that natural factors were the main reason restricting the coordinated development of PLES, and socioeconomic factors could effectively promote the coordinated development of PLES. Landscape pattern also significantly influenced the coordinated development of PLES, but varied greatly. The findings of this study can provide a scientific reference for the optimization of territorial space layout and the promotion of high-quality development of territorial space.

Evaluation of an OCT-AI-Based Telemedicine Platform for Retinal Disease Screening and Referral in a Primary Care Setting.

PURPOSE: To evaluate the performance of a telemedicine platform integrated with optical coherence tomography (OCT) and artificial intelligence (AI) techniques for retinal disease screening and referral. METHODS: We constructed an OCT-AI-based telemedicine platform and deployed it at four primary care stations located in Jing'an district, Shanghai, to detect retinal disease cases among aged groups and refer them to Shanghai Tenth People's Hospital (TENTH Hospital). Two ophthalmologists jointly graded the data set collected from this pilot application, and then the performance of this platform was analyzed from multiple aspects. RESULTS: This study included 1257 participants between July 2020 and September 2020, of whom 394 had retinal pathologies and 146 were even considered urgent cases by the ophthalmologists. The OCT-AI models achieved a sensitivity of 96.6% (95% confidence interval [CI], 91.8%-98.7%) and specificity of 98.8% (95% CI, 98.0%-99.3%) for detecting urgent cases and a sensitivity of 98.5% (95% CI, 96.5%-99.4%) and specificity of 96.2% (95% CI, 94.6%-97.3%) for detecting both urgent and routine cases. Coupled with AI, our platform reduced the workload of human consultation by 96.2% for massive normal cases. The detected disease cases received online medical suggestions at an average time of 21.4 hours via this platform. CONCLUSIONS: This platform can automatically identify patients with retinal disease with high sensitivity and specificity, support timely human consultation, and bring necessary referrals. TRANSLATIONAL RELEVANCE: The OCT-AI-based telemedicine platform shows great practical value for retinal disease screening and referral in a real-world primary care setting.

Targeting Proliferating Tumor-Infiltrating Macrophages Facilitates Spatial Redistribution of CD8+ T Cells in Pancreatic Cancer.

Tumor-associated macrophages (TAMs) play crucial roles in cancer progression, but the contributions and regulation of different macrophage subpopulations remain unclear. Here, we report a high level of TAM infiltration in human and mouse pancreatic ductal adenocarcinoma (PDAC) models and that the targeting of proliferating F4/80+ macrophages facilitated cytotoxic CD8+ T-cell-dependent antitumor immune responses. A well-defined KPC-derived PDAC cell line and the murine Panc02 PDAC cell line were used. Treatment of PDAC-bearing mice with clodronate liposomes, an agent that chemically depletes macrophages, did not impact macrophage subpopulations in the local tumor microenvironment (TME). However, further investigation using both BrdU and Ki67 to evaluate proliferating cells showed that clodronate liposomes treatment reduced proliferating macrophages in the KPC and Panc02 models. We further evaluated the distance between CD8+ T cells and PanCK+ tumor cells, and clodronate liposomes treatment significantly increased the number of CD8+ T cells in close proximity (<30 µm) to PanCK+ PDAC cells, with increased numbers of tumor-infiltrating IFN-γ+CD8+ T cells. This study suggests that targeting proliferating tumor-infiltrating macrophages may increase CD8+ cytotoxic lymphocyte (CTL) infiltration and facilitate the spatial redistribution of CD8+ T cells in tumors, contributing to the antitumor effect.

Overexpression of PLXDC2 in Stromal Cell-Associated M2 Macrophages Is Related to EMT and the Progression of Gastric Cancer.

The tumor microenvironment (TME) comprises distinct cell types, including stromal types such as fibroblast cells and macrophage cells, which have recently become a critical factor in tumor development and progression. Here, we identified the TME-related gene, plexin domain containing 2 (PLXDC2), in a high-stromal-score population. And we revealed that this gene was related to poor survival and advanced (tumor-node-metastasis) stage in gastric cancer (GC) patients from The Cancer Genome Atlas database. An integrated gene profile and functional analysis of the proportions of tumor-infiltrating immune cells revealed that the expression of the M2 macrophages cell marker CD163 was positively correlated with PLXDC2 expression. In addition, the M2 macrophages gene signature and high PLXDC2 expression were associated with the inflammatory signaling pathway and the epithelial-to-mesenchymal transition (EMT)-related gene signature. Single-cell study of GC identified PLXDC2 was enriched specifically in fibroblasts and monocytes/macrophages populations, which supported its important role in the stroma. Furthermore, according to a tissue microarray immunohistochemistry analysis, the expression of PLXDC2 elevated in human GC stromal specimens compared to tumor tissue specimens. Moreover, PLXDC2 overexpression in the stromal compartment was associated with CD163-positive regulatory M2 macrophages, and its functions were related to the pathogenesis of GC. Multiplexed immunohistochemistry verified PLXDC2's correlation with EMT markers. Our data suggested that PLXDC2 was expressed in stromal cells and that its crosstalk with tumor-associated macrophages could contribute to cancer biology by inducing the EMT process.

CXCL8 Associated Dendritic Cell Activation Marker Expression and Recruitment as Indicators of Favorable Outcomes in Colorectal Cancer.

Accumulating evidence suggests that tumor-infiltrating immune cells (TICs) in the tumor microenvironment (TME) serve as promising therapeutic targets. CXCL8 (IL-8) may also be a potential therapeutic target in cancer. CXCL8 is a potent chemotactic factor for neutrophils, myeloid-derived suppressor cells (MDSCs) and monocytes, which are considered immunosuppressive components in cancer-bearing hosts. Here, we identified the TME-related gene CXCL8 in a high-ImmuneScore population that contributed to better survival in colorectal cancer (CRC) patients from The Cancer Genome Atlas (TCGA) database. An integrated gene profile and functional analysis of TIC proportions revealed that the dendritic cell (DC) activation markers CD80, CD83, and CD86 were positively correlated with CXCL8 expression, suggesting that CXCL8 may be functional as antitumor immune response status in the TME. The gene signature was further validated in independent GSE14333 and GSE38832 cohorts from the Gene Expression Omnibus (GEO). To test the differential contributions of immune and tumor components to progression, three CRC cell lines, CT26, MC38 and HCT116, were used. In vitro results suggested no significant growth or survival changes following treatment with an inhibitor of the CXCL8 receptor (CXCR1/2) such as reparixin or danirixin. In vivo treatment with danirixin (antagonists of CXCR2) promoted tumor progression in animal models established with CT26 cells. CXCR2 antagonism may function via an immune component, with CXCR2 antagonist treatment in mice resulting in reduced activated DCs and correlating with decreased Interferon gamma (IFN-γ) or Granzyme B expressed CD8+ T cells. Furthermore, CXCL8 induced DC migration in transwell migration assays. Taken together, our data suggested that targeting the CXCL8-CXCR2 axis might impede DC activation or recruitment, and this axis could be considered a favorable factor rather than a target for critical antitumor effects on CRC.

The Relationship between Sparseness and Energy Consumption of Neural Networks.

About 50-80% of total energy is consumed by signaling in neural networks. A neural network consumes much energy if there are many active neurons in the network. If there are few active neurons in a neural network, the network consumes very little energy. The ratio of active neurons to all neurons of a neural network, that is, the sparseness, affects the energy consumption of a neural network. Laughlin's studies show that the sparseness of an energy-efficient code depends on the balance between signaling and fixed costs. Laughlin did not give an exact ratio of signaling to fixed costs, nor did they give the ratio of active neurons to all neurons in most energy-efficient neural networks. In this paper, we calculated the ratio of signaling costs to fixed costs by the data from physiology experiments. The ratio of signaling costs to fixed costs is between 1.3 and 2.1. We calculated the ratio of active neurons to all neurons in most energy-efficient neural networks. The ratio of active neurons to all neurons in neural networks is between 0.3 and 0.4. Our results are consistent with the data from many relevant physiological experiments, indicating that the model used in this paper may meet neural coding under real conditions. The calculation results of this paper may be helpful to the study of neural coding.

An Intelligent Optical Coherence Tomography-based System for Pathological Retinal Cases Identification and Urgent Referrals.

Purpose: This study aimed to develop an automated system with artificial intelligence algorithms to comprehensively identify pathologic retinal cases and make urgent referrals. Methods: To build and test the intelligent system, this study obtained 28,664 optical coherence tomography (OCT) images from 2254 patients in the Eye and ENT Hospital of Fudan University (EENT Hospital) and Shanghai Tenth People's Hospital (TENTH Hospital). We applied a deep learning model with an adapted feature pyramid network to detect 15 categories of retinal pathologies from OCT images as common signs of various retinal diseases. Subsequently, the pathologies detected in the OCT images and thickness features extracted from retinal thickness measurements were combined for urgent referral using the random forest tool. Results: The retinal pathologies detection model had a sensitivity of 96.39% and specificity of 98.91% from the EENT Hospital test dataset, whereas those from the TENTH Hospital test dataset were 94.89% and 98.76%, respectively. The urgent referral model achieved accuracies of 98.12% and 98.01% from the EENT Hospital and TENTH Hospital test datasets, respectively. Conclusions: An intelligent system capable of automatically identifying pathologic retinal cases and offering urgent referrals was developed and demonstrated reliable performance with high sensitivity, specificity, and accuracy. Translational Relevance: This intelligent system has great value and practicability in communities where exist increasing cases of retinal disease and a lack of ophthalmologists.

Risk factors and intestinal microbiota: Clostridioides difficile infection in patients receiving enteral nutrition at Intensive Care Units.

BACKGROUND: Clostridioides difficile infection (CDI) is a leading cause of nosocomial diarrhea. Patients receiving enteral nutrition (EN) in the intensive care unit (ICU) are potentially at high risk of CDI. In the present study, we assessed the risk factors and intestinal microbiome of patients to better understand the occurrence and development of CDI. METHODS: Patients were screened for C. difficile every week after starting EN, and their clinical records were collected for risk factor identification. Fecal samples were analyzed using 16S rRNA sequencing to evaluate the intestinal microbiota. RESULTS: Overall incidence of CDI was 10.7% (18/168 patients). History of cerebral infarction was significantly associated with CDI occurrence (OR, 9.759; 95% CI, 2.140-44.498), and treatment with metronidazole was identified to be protective (OR, 0.287; 95% CI, 0.091-0.902). Patients with EN had lower bacterial richness and diversity, accompanied by a remarkable decrease in the abundance of Bacteroides, Prevotella_9, Ruminococcaceae, and Lachnospiraceae. Of these patients, acquisition of C. difficile resulted in a transient increase in microbial diversity, along with consistent alterations in the proportion of some bacterial taxa, especially Ruminococcaceae and Lachnospiraceae. Upon initiation of EN, patients who were positive for C. difficile later showed an enhanced load of Bacteroides, which was negatively correlated with the abundance of C. difficile when CDI developed. CONCLUSION: ICU patients receiving EN have a high prevalence of CDI and a fragile intestinal microbial environment. History of cerebral infarction and prior treatment with metronidazole are considered as vital risk and protective factors, respectively. We propose that the emergence of CDI could cause a protective alteration of the intestinal microbiota. Additionally, Bacteroides loads seem to be closely related to the occurrence and development of CDI.

Simulation of retinal ganglion cell response using fast independent component analysis.

Advances in neurobiology suggest that neuronal response of the primary visual cortex to natural stimuli may be attributed to sparse approximation of images, encoding stimuli to activate specific neurons although the underlying mechanisms are still unclear. The responses of retinal ganglion cells (RGCs) to natural and random checkerboard stimuli were simulated using fast independent component analysis. The neuronal response to stimuli was measured using kurtosis and Treves-Rolls sparseness, and the kurtosis, lifetime and population sparseness were analyzed. RGCs exhibited significant lifetime sparseness in response to natural stimuli and random checkerboard stimuli. About 65 and 72% of RGCs do not fire all the time in response to natural and random checkerboard stimuli, respectively. Both kurtosis of single neurons and lifetime response of single neurons values were larger in the case of natural than in random checkerboard stimuli. The population of RGCs fire much less in response to random checkerboard stimuli than natural stimuli. However, kurtosis of population sparseness and population response of the entire neurons were larger with natural than random checkerboard stimuli. RGCs fire more sparsely in response to natural stimuli. Individual neurons fire at a low rate, while the occasional "burst" of neuronal population transmits information efficiently.