The associations between peripheral inflammatory and lipid parameters, white matter hyperintensity, and cognitive function in patients with non-disabling ischemic cerebrovascular events.

BACKGROUND: The global prevalence of VCI has increased steadily in recent years, but diagnostic biomarkers for VCI in patients with non-disabling ischemic cerebrovascular incidents (NICE) remain indefinite. The primary objective of this research was to investigate the relationship between peripheral serological markers, white matter damage, and cognitive function in individuals with NICE. METHODS: We collected clinical data, demographic information, and medical history from 257 patients with NICE. Using the MoCA upon admission, patients were categorized into either normal cognitive function (NCF) or VCI groups. Furthermore, they were classified as having mild white matter hyperintensity (mWMH) or severe WMH based on Fazekas scores. We then compared the levels of serological markers between the cognitive function groups and the WMH groups. RESULTS: Among 257 patients with NICE, 165 were male and 92 were female. Lymphocyte count (OR = 0.448, P < 0.001) and LDL-C/HDL-C (OR = 0.725, P = 0.028) were protective factors for cognitive function in patients with NICE. The sWMH group had a higher age and inflammation markers but a lower MoCA score, and lymphocyte count than the mWMH group. In the mWMH group, lymphocyte count (AUC = 0.765, P < 0.001) and LDL-C/HDL-C (AUC = 0.740, P < 0.001) had an acceptable diagnostic value for the diagnosis of VCI. In the sWMH group, no significant differences were found in serological markers between the NCF and VCI groups. CONCLUSION: Lymphocyte count, LDL-C/HDL-C were independent protective factors for cognitive function in patients with NICE; they can be used as potential biological markers to distinguish VCI in patients with NICE and are applicable to subgroups of patients with mWMH.

Lower 25-hydroxyvitamin D is associated with severer white matter hyperintensity and cognitive function in patients with non-disabling ischemic cerebrovascular events.

OBJECTIVES: This study aimed to investigate the potential correlations among serum 25-hydroxyvitamin D [25(OH)D] levels, white matter hyperintensity (WMH) and cognitive function in patients with non-disabling ischemic cerebrovascular events (NICE). METHODS: This was a prospective investigation of 160 NICE patients with age of 40 years or older. Cognitive function was evaluated by the Montreal Cognitive Assessment (MoCA). White matter lesions were evaluated by WMH using Fazekas scores. Spearman correlation analysis and linear regression models were used to identify the associations between serum 25(OH)D levels and cognitive function. Binary logistic regression analysis models were used to evaluate the predictable value of serum 25(OH)D levels and WMH for cognitive impairment. RESULTS: Patients with inadequate 25(OH)D levels had lower MoCA score (P=0.008), and a higher proportion of severe WMH (P=0.043). Spearman correlation analysis demonstrated that serum 25(OH)D concentrations were positively associated with MoCA score (rs=0.185, P=0.019) while negatively related to the proportion of severe WMH (sWMH) (rs=-0.166, P=0.036).The association between 25(OH)D concentrations and MoCA score remained significant in linear regression (adjusted ß=0.012, 95%CI:0.001-0.203).Adjusted binary logistic regression analysis showed that the odds ratio of cognitive impairment with insufficient 25(OH)D concentration was 5.038 (95%CI:1.154-21.988) compared with the sufficient group and the sWMH (OR=2.728, 95%CI:1.230-6.051) was identified as an independent risk factor for cognitive decline in NICE patients. CONCLUSION: Serum 25(OH)D levels and white matter lesions were independently and significantly associated with cognitive impairment in NICE patients.

Exploring angular-steering illumination-based eyebox expansion for holographic displays.

Holography represents an enabling technology for next-generation virtual and augmented reality systems. However, it remains challenging to achieve both wide field of view and large eyebox at the same time for holographic near-eye displays, mainly due to the essential étendue limitation of existing hardware. In this work, we present an approach to expanding the eyebox for holographic displays without compromising their underlying field of view. This is achieved by utilizing a compact 2D steering mirror to deliver angular-steering illumination beams onto the spatial light modulator in alignment with the viewer's eye movements. To facilitate the same image for the virtual objects perceived by the viewer when the eye moves, we explore an off-axis computational hologram generation scheme. Two bench-top holographic near-eye display prototypes with the proposed angular-steering scheme are developed, and they successfully showcase an expanded eyebox up to 8 mm × 8 mm for both VR- and AR-modes, as well as the capability of representing multi-depth holographic images.

Investigating learning-empowered hologram generation for holographic displays with ill-tuned hardware.

Existing computational holographic displays often suffer from limited reconstruction image quality mainly due to ill-conditioned optics hardware and hologram generation software. In this Letter, we develop an end-to-end hardware-in-the-loop approach toward high-quality hologram generation for holographic displays. Unlike other hologram generation methods using ideal wave propagation, ours can reduce artifacts introduced by both the light propagation model and the hardware setup, in particular non-uniform illumination. Experimental results reveal that, compared with classical computer-generated hologram algorithm counterparts, better quality of holographic images can be delivered without a strict requirement on both the fine assembly of optical components and the good uniformity of laser sources.

Diffraction model-informed neural network for unsupervised layer-based computer-generated holography.

Learning-based computer-generated holography (CGH) has shown remarkable promise to enable real-time holographic displays. Supervised CGH requires creating a large-scale dataset with target images and corresponding holograms. We propose a diffraction model-informed neural network framework (self-holo) for 3D phase-only hologram generation. Due to the angular spectrum propagation being incorporated into the neural network, the self-holo can be trained in an unsupervised manner without the need of a labeled dataset. Utilizing the various representations of a 3D object and randomly reconstructing the hologram to one layer of a 3D object keeps the complexity of the self-holo independent of the number of depth layers. The self-holo takes amplitude and depth map images as input and synthesizes a 3D hologram or a 2D hologram. We demonstrate 3D reconstructions with a good 3D effect and the generalizability of self-holo in numerical and optical experiments.

Correlations among peripheral blood markers, white matter hyperintensity, and cognitive function in patients with non-disabling ischemic cerebrovascular events.

Background: Both inflammation and cerebral white matter injury are closely associated with vascular cognitive impairment (VCI). The aim of this study was to analyze the correlation between peripheral serological markers, white matter injury, and cognitive function in patients with non-disabling ischemic cerebrovascular events (NICE); to identify potential biological markers for the diagnosis and prediction of VCI; and to provide a basis for the early diagnosis and intervention of VCI. Methods: We collected clinical data, along with demographic and medical history data, from 151 NICE patients. Fasting venous blood samples were collected. Based on the Montreal Cognitive Assessment (MoCA) after admission, we divided the patients into normal cognitive function (NCF) and VCI groups, and then classified them into mild white matter hyperintensity (mWMH) and severe white matter hyperintensity (sWMH) based on Fazekas scores. The differences in serological marker levels were compared between the cognitive function groups and the white matter hyperintensity groups. Binary logistic regression models and receiver operating characteristic curves were used to analyze the diagnostic predictive value of serological markers for VCI in patients with NICE and in the white matter hyperintensity subgroups. Results: Among 151 patients with NICE, 95 were male and 56 were female. Lymphocyte count (OR = 0.405, p = 0.010, 95% CI [0.201, 0.806]), red blood cell count (OR = 0.433, p = 0.010, 95% CI [0.228, 0.821]), and hemoglobin level (OR = 0.979, p = 0.046, 95% CI [0.958, 0.999]) were protective factors for cognitive function in patients with NICE. The sWMH group had a higher age, granulocyte/lymphoid ratio (NLR), and neutrophil percentage but a lower MoCA score, hemoglobin level, and lymphocyte count than the mWMH group. In the mWMH group, lymphocyte count (AUC = 0.713, p = 0.003, 95% CI [0.593, 0.833]) had an acceptable predictive value for the diagnosis of VCI, whereas white blood cell count (AUC = 0.672, p = 0.011, 95% CI [0.545, 0.799]), red blood cell count (AUC = 0.665, p = 0.014, 95% CI [0.545, 0.784]), and hemoglobin level (AUC = 0.634, p = 0.047, 95% CI [0.502, 0.765]) had marginal predictive value for the diagnosis of VCI. In the sWMH group, no significant differences were found in serological markers between the NCF and VCI groups. Conclusion: Lymphocyte count, red blood cell count, and hemoglobin level were independent protective factors for cognitive function in patients with NICE; they can be used as potential biological markers to distinguish VCI in patients with NICE and are applicable to subgroups of patients with mWMH.

Learning Two-Stream CNN for Multi-Modal Age-Related Macular Degeneration Categorization.

This paper tackles automated categorization of Age-related Macular Degeneration (AMD), a common macular disease among people over 50. Previous research efforts mainly focus on AMD categorization with a single-modal input, let it be a color fundus photograph (CFP) or an OCT B-scan image. By contrast, we consider AMD categorization given a multi-modal input, a direction that is clinically meaningful yet mostly unexplored. Contrary to the prior art that takes a traditional approach of feature extraction plus classifier training that cannot be jointly optimized, we opt for end-to-end multi-modal Convolutional Neural Networks (MM-CNN). Our MM-CNN is instantiated by a two-stream CNN, with spatially-invariant fusion to combine information from the CFP and OCT streams. In order to visually interpret the contribution of the individual modalities to the final prediction, we extend the class activation mapping (CAM) technique to the multi-modal scenario. For effective training of MM-CNN, we develop two data augmentation methods. One is GAN-based CFP/OCT image synthesis, with our novel use of CAMs as conditional input of a high-resolution image-to-image translation GAN. The other method is Loose Pairing, which pairs a CFP image and an OCT image on the basis of their classes instead of eye identities. Experiments on a clinical dataset consisting of 1,094 CFP images and 1,289 OCT images acquired from 1,093 distinct eyes show that the proposed solution obtains better F1 and Accuracy than multiple baselines for multi-modal AMD categorization. Code and data are available at https://github.com/li-xirong/mmc-amd.

A novel diagnostic model for predicting immune microenvironment subclass based on costimulatory molecules in lung squamous carcinoma.

There is still no ideal predictive biomarker for immunotherapy response among patients with non-small cell lung cancer. Costimulatory molecules play a role in anti-tumor immune response. Hence, they can be a potential biomarker for immunotherapy response. The current study comprehensively investigated the expression of costimulatory molecules in lung squamous carcinoma (LUSC) and identified diagnostic biomarkers for immunotherapy response. The costimulatory molecule gene expression profiles of 627 patients were obtained from the The Cancer Genome Atlas, GSE73403, and GSE37745 datasets. Patients were divided into different clusters using the k-means clustering method and were further classified into two discrepant tumor microenvironment (TIME) subclasses (hot and cold tumors) according to the immune score of the ESTIMATE algorithm. A high proportion of activated immune cells, including activated memory CD4 T cells, CD8 T cells, and M1 macrophages. Five CMGs (FAS, TNFRSF14, TNFRSF17, TNFRSF1B, and TNFSF13B) were considered as diagnostic markers using the Least Absolute Shrinkage and Selection Operator and the Support Vector Machine-Recursive Feature Elimination machine learning algorithms. Based on the five CMGs, a diagnostic nomogram for predicting individual tumor immune microenvironment subclasses in the TCGA dataset was developed, and its predictive performance was validated using GSE73403 and GSE37745 datasets. The predictive accuracy of the diagnostic nomogram was satisfactory in all three datasets. Therefore, it can be used to identify patients who may benefit more from immunotherapy.

Automated diagnoses of age-related macular degeneration and polypoidal choroidal vasculopathy using bi-modal deep convolutional neural networks.

AIMS: To investigate the efficacy of a bi-modality deep convolutional neural network (DCNN) framework to categorise age-related macular degeneration (AMD) and polypoidal choroidal vasculopathy (PCV) from colour fundus images and optical coherence tomography (OCT) images. METHODS: A retrospective cross-sectional study was proposed of patients with AMD or PCV who came to Peking Union Medical College Hospital. Diagnoses of all patients were confirmed by two retinal experts based on diagnostic gold standard for AMD and PCV. Patients with concurrent retinal vascular diseases were excluded. Colour fundus images and spectral domain OCT images were taken from dilated eyes of patients and healthy controls, and anonymised. All images were pre-labelled into normal, dry or wet AMD or PCV. ResNet-50 models were used as the backbone and alternate machine learning models including random forest classifiers were constructed for further comparison. For human-machine comparison, the same testing data set was diagnosed by three retinal experts independently. All images from the same participant were presented only within a single partition subset. RESULTS: On a test set of 143 fundus and OCT image pairs from 80 eyes (20 eyes per-group), the bi-modal DCNN demonstrated the best performance, with accuracy 87.4%, sensitivity 88.8% and specificity 95.6%, and a perfect agreement with diagnostic gold standard (Cohen's κ 0.828), exceeds slightly over the best expert (Human1, Cohen's κ 0.810). For recognising PCV, the model outperformed the best expert as well. CONCLUSION: A bi-modal DCNN for automated classification of AMD and PCV is accurate and promising in the realm of public health.

Prediction of OCT images of short-term response to anti-VEGF treatment for neovascular age-related macular degeneration using generative adversarial network.

BACKGROUND/AIMS: The aim of this study was to generate and evaluate individualised post-therapeutic optical coherence tomography (OCT) images that could predict the short-term response of antivascular endothelial growth factor therapy for typical neovascular age-related macular degeneration (nAMD) based on pretherapeutic images using generative adversarial network (GAN). METHODS: A total of 476 pairs of pretherapeutic and post-therapeutic OCT images of patients with nAMD were included in training set, while 50 pretherapeutic OCT images were included in the tests set retrospectively, and their corresponding post-therapeutic OCT images were used to evaluate the synthetic images. The pix2pixHD method was adopted for image synthesis. Three experiments were performed to evaluate the quality, authenticity and predictive power of the synthetic images by retinal specialists. RESULTS: We found that 92% of the synthetic OCT images had sufficient quality for further clinical interpretation. Only about 26%-30% synthetic post-therapeutic images could be accurately identified as synthetic images. The accuracy to predict macular status of wet or dry was 0.85 (95% CI 0.74 to 0.95). CONCLUSION: Our results revealed a great potential of GAN to generate post-therapeutic OCT images with both good quality and high accuracy.

Protective effect of rapamycin on endothelial-to-mesenchymal transition in HUVECs through the Notch signaling pathway.

Neointima hyperplasia is one of the predominant features of cardiovascular diseases such as atherosclerosis, and is also responsible for the restenosis of vascular surgery including arteriovenous fistula and stent implantation. Endothelial-to-mesenchymal transition (EndMT) contributes to neointima hyperplasia by activation of the Notch or TGF-ß signaling pathway. Rapamycin has been utilized as anti-restenosis drug due to its anti-proliferative activity. However, its effects on the EndMT have not been investigated yet. Thus, we examined the biological effects of rapamycin on the EndMT and its potential mechanisms. We showed that rapamycin significantly reversed TGF-ß1 stimulated EndMT by upregulating endothelial marker CD31 expression and downregulating mesenchymal marker SMA-α expression in human umbilical vein endothelial cells (HUVECs). Rapamycin also inhibited TGF-ß1 induced expression of the Notch signaling pathway components expression, such as Notch-1, Jagged-1, RBP-jκ and Hes-5. Among the different Notch receptors and ligands, Jagged-1/Notch-1 cascade was most remarkably blocked by rapamycin. Finally, consistently with the results from Notch inhibitor DAPT treatment, rapamycin suppressed the migration of HUVECs in vitro. Together, these findings indicate that rapamycin may function as an effective inhibitor of the EndMT in HUVECs by suppressing targeting the Notch signaling pathway.

Integrin ß3 Mediates the Endothelial-to-Mesenchymal Transition via the Notch Pathway.

BACKGROUND/AIMS: Neointimal hyperplasia is responsible for stenosis, which requires corrective vascular surgery, and is also a major morphological feature of many cardiovascular diseases. This hyperplasia involves the endothelial-to-mesenchymal transition (EndMT). We investigated whether integrin ß3 can modulate the EndMT, as well as its underlying mechanism. METHODS: Integrin ß3 was overexpressed or knocked down in human umbilical vein endothelial cells (HUVECs). The expression of endothelial markers and mesenchymal markers was determined by real-time reverse transcription PCR (RT-PCR), immunofluorescence staining, and western blot analysis. Notch signaling pathway components were detected by real-time RT-PCR and western blot analysis. Cell mobility was evaluated by wound-healing, Transwell, and spreading assays. Fibroblast-specific protein 1 (FSP-1) promoter activity was determined by luciferase assay. RESULTS: Transforming growth factor (TGF)-ß1 treatment or integrin ß3 overexpression significantly promoted the EndMT by downregulating VE-cadherin and CD31 and upregulating smooth muscle actin α and FSP-1 in HUVECs, and by enhancing cell migration. Knockdown of integrin ß3 reversed these effects. Notch signaling was activated after TGF-ß1 treatment of HUVECs. Knockdown of integrin ß3 suppressed TGF-ß1-induced Notch activation and expression of the Notch downstream target FSP-1. CONCLUSION: Integrin ß3 may promote the EndMT in HUVECs through activation of the Notch signaling pathway.

MiR-142-3p as a potential prognostic biomarker for esophageal squamous cell carcinoma.

BACKGROUND AND OBJECTIVES: microRNAs (miRNAs), small non-coding RNAs, are always aberrantly expressed in many diseases including human cancers. The aim of this study was to examine and determine the clinical significance of hsa-miR-31, hsa-miR-142-3p, hsa-miR-338-3p, and hsa-miR-1261 expression in esophageal squamous cell carcinoma (ESCC). METHODS: Expression levels of four selected miRNAs, initially evaluated by microarray, were validated by qRT-PCR. Various statistical methods were used to analyze the relationship between miRNA expression and clinicopathologic features and prognosis in 91 patients with ESCC. RESULTS: MiR-31 and miR-142-3p expression were correlated to histological differentiation in ESCC (P < 0.05, Student's t-test); high miR-142-3p expression was associated with a poor prognosis in all 91 ESCC patients (P = 0.014, log-rank) and identified as an independent prognostic factor in ESCC (P = 0.017, univariate Cox; P = 0.022, multivariate Cox). More importantly, stratified analysis indicated that high miR-142-3p expression was correlated to a poor prognosis within good-prognosis groups comprised of ESCC patients with small tumor size, negative lymph node metastasis, or early stage (all P < 0.05). CONCLUSION: The main findings suggest that miR-142-3p is involved in the progression of ESCC and is a potential prognostic biomarker for ESCC.

The expression of CFL1 and N-WASP in esophageal squamous cell carcinoma and its correlation with clinicopathological features.

Cofilin1 (CFL1) is an actin-modulating protein, which belongs to the ADF/Cofilin family. Neural Wiskott-Aldrich syndrome protein (N-WASP) is the key regulator of the actin cytoskeleton, a member of Wiskott-Aldrich syndrome protein family. They have been suggested to be involved in cancer cell invasion and metastasis. In this study, the expression patterns of CFL1 and N-WASP in normal esophageal mucosa and esophageal squamous cell carcinoma (ESCC) and their correlation with clinical characteristics were investigated. Immunohistochemical staining showed that CFL1 was expressed in nuclear and cytoplasm of cancer cells. However, N-WASP was mainly found in the cytoplasm of the cancer cells. There were significant evidences that proved that CFL1 is correlated with clinicopathological factors in ESCC, such as infiltration depth, lymph node metastasis and pathological staging (P < 0.05). It is also proved that N-WASP is related to lymph node metastasis and pathological staging in ESCC (P < 0.05). Kaplan-Meier analysis showed that there was no correlation between CFL1 and N-WASP protein expression and survival (P > 0.05). Moreover, the mRNA expression of CFL1 and N-WASP was detected by quantitative real time PCR in 70 tissue specimens. The results showed that CFL1 mRNA level was over-expressed in ESCC tissue (P < 0.05), while N-WASP mRNA expression level was not different between cancerous tissues and adjacent normal esophageal mucosa (P > 0.05). Also, CFL1 mRNA expression was significantly associated with regional lymph node metastasis and pathological staging (P < 0.05). Kaplan-Meier analysis showed that there was no correlation between CFL1 and N-WASP mRNA expression and survival (P > 0.05). Our findings suggested that CFL1 and N-WASP may play an important role in the tumorigenesis of ESCC, and to be the candidate novel biomarkers for the diagnosis and prognosis of ESCC. These findings may have implications for targeted therapies in patients with ESCC.